Methods and apparatus for discriminative semantic transfer and physics-inspired optimization of features in deep learning

ABSTRACT

Methods and apparatus for discriminative semantic transfer and physics-inspired optimization in deep learning are disclosed. A computation training method for a convolutional neural network (CNN) includes receiving a sequence of training images in the CNN of a first stage to describe objects of a cluttered scene as a semantic segmentation mask. The semantic segmentation mask is received in a semantic segmentation network of a second stage to produce semantic features. Using weights from the first stage as feature extractors and weights from the second stage as classifiers, edges of the cluttered scene are identified using the semantic features.

PRIORITY

This application claims priority and the benefit of U.S. ProvisionalPatent Application No. 62/509,960, entitled “METHODS AND APPARATUS FORDISCRIMINATIVE SEMANTIC TRANSFER FEATURES IN DEEP LEARNING,” filed onMay 23, 2017, and U.S. Provisional Patent Application No. 62/509,990,entitled “METHODS AND APPARATUS FOR PHYSICS-INSPIRED OPTIMIZATION OFFEATURES IN DEEP LEARNING,” filed on May 23, 2017, both of which arehereby incorporated by reference and commonly assigned.

FIELD

Embodiments relate generally to data processing and more particularly todata processing via a general-purpose graphics processing unit. Inparticular, embodiments relate to systems and methods for executing adeep learning network with discriminative semantic transfer andphysics-inspired optimization of features in deep learning.

BACKGROUND

Current parallel graphics data processing includes systems and methodsdeveloped to perform specific operations on graphics data such as, forexample, linear interpolation, tessellation, rasterization, texturemapping, depth testing, etc. Traditionally, graphics processors usedfixed function computational units to process graphics data; however,more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data.

To further increase performance, graphics processors typically implementprocessing techniques such as pipelining that attempt to process, inparallel, as much graphics data as possible throughout the differentparts of the graphics pipeline. Parallel graphics processors with singleinstruction, multiple thread (SIMT) architectures are designed tomaximize the amount of parallel processing in the graphics pipeline. Inan SIMT architecture, groups of parallel threads attempt to executeprogram instructions synchronously together as often as possible toincrease processing efficiency. A general overview of software andhardware for SIMT architectures can be found in Shane Cook, CUDAProgramming Chapter 3, pages 37-51 (2013).

Machine learning has been successful at solving many kinds of tasks. Thecomputations that arise when training and using machine learningalgorithms (e.g., neural networks) lend themselves naturally toefficient parallel implementations. Accordingly, parallel processorssuch as general-purpose graphic processing units (GPGPUs) have played asignificant role in the practical implementation of deep neuralnetworks. Parallel graphics processors with single instruction, multiplethread (SIMT) architectures are designed to maximize the amount ofparallel processing in the graphics pipeline. In an SIMT architecture,groups of parallel threads attempt to execute program instructionssynchronously together as often as possible to increase processingefficiency. The efficiency provided by parallel machine learningalgorithm implementations allows the use of high capacity networks andenables those networks to be trained on larger datasets.

A deep learning neural network (DNN) is typically structured as a typeof convolutional neural network and is used to perform complexassociative tasks. After a training phase using known inputs, the DNN isable to recognize new inputs that are similar to the original traininginput. This is helpful for object detection technologies, automaticspeech recognition, user authentication, image understanding, andmachine vision uses, among others. A video sequence may be used forobject tracking as well as for recognition.

BRIEF DESCRIPTION OF THE DRAWINGS

The appended drawings illustrate examples and are, therefore, exemplaryembodiments and not considered to be limiting in scope.

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of exemplary embodiments described herein.

FIGS. 2A-2D illustrate a parallel processor components according to anexemplary embodiment.

FIGS. 3A-3B are block diagrams of graphics multiprocessors according toexemplary embodiments.

FIGS. 4A-4F illustrate an exemplary architecture in which a plurality ofGraphic Processing Units (GPUs) are communicatively coupled to aplurality of multi-core processors.

FIG. 5 illustrates a graphics processing pipeline according to anexemplary embodiment.

FIG. 6 illustrates a machine learning software stack according to anexemplary embodiment.

FIG. 7 illustrates a highly-parallel general-purpose graphics processingunit according to an exemplary embodiment.

FIG. 8 illustrates a multi-GPU computing system according to anexemplary embodiment.

FIGS. 9A-9B illustrate layers of exemplary deep neural networks.

FIG. 10 illustrates an exemplary recurrent neural network.

FIG. 11 illustrates exemplary embodiment of training and deployment of adeep neural network.

FIG. 12 is an exemplary block diagram illustrating distributed learning.

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC)suitable for performing inferencing using a trained model.

FIG. 14 is an exemplary diagram of storing RGB and CNN channel data aspart of a deep channel image is an exemplary block diagram of an imagecapturing system storing a deep channel image using a ConvolutionalNeural Network (CNN).

FIG. 15 is an overview diagram of sequential operations for semantictransfer features in deep learning network according to exemplaryembodiments.

FIG. 16 is a diagram of probabilistic node connectivity for semantictransfer features according to exemplary embodiments.

FIG. 17 is a diagram of a network design for semantic segmentation,semantic transfer, semantic feature space, and transfer weightsvisualization according to exemplary embodiments.

FIG. 18A is a diagram on the left of feature quality visualization. Fromtop to bottom: input, bg activation, wf activation, ww activation, wcactivation, softmax results. On the right is a feature qualitycomparison.

FIG. 18B is a diagram of a topology of a room layout from a test setaccording to an exemplary embodiment.

FIG. 18C is a diagram of a room shown in an input image, feature mapconstructed by considering an edge as a spring and the feature map as apotential field and correlating forces imposed on the spring's everypoint, and influence of force composition according to exemplaryembodiments.

FIG. 18D shows examples of qualitative results on an LSUN validation seton the left as visualized feature map merges of wf, ww, and wc by apixel-wise max operation according to exemplary embodiments. The rightside shows typical failure cases in which a wrong topology produces thelowest energy according to exemplary embodiments.

FIG. 18E shows examples of qualitative results on a Hedau validation seton the left as visualized feature map merges of wf, ww, and wc by apixel-wise max operation according to exemplary embodiments.

FIG. 18F is a graph of error versus window size on the LSUN validationset according to exemplary embodiments.

FIG. 18G is a diagram of a comparison of two techniques forphysics-inspired optimization according to exemplary embodiments.

FIG. 19 illustrates a block diagram of a processing system according toan exemplary embodiment.

FIG. 20 illustrates an exemplary block diagram of an embodiment of aprocessor having one or more processor cores, an integrated memorycontroller, and an integrated graphics processor.

FIG. 21 illustrates an exemplary block diagram of a graphics processor.

FIG. 22 illustrates a block diagram of a graphics processing engine of agraphics processor according to exemplary embodiments.

FIG. 23 illustrates a block diagram of another exemplary embodiment of agraphics processor.

FIG. 24 illustrates thread execution logic including an array ofprocessing elements employed in exemplary embodiments of a graphicsprocessing engine (GPE).

FIG. 25 illustrates a block diagram of a graphics processor instructionformats according to exemplary embodiments.

FIG. 26 illustrates a block diagram of an exemplary embodiment of agraphics processor.

FIG. 27A illustrates a block diagram of a graphics processor commandformat according to an exemplary embodiment.

FIG. 27B illustrates a block diagram of a graphics processor commandsequence according to an exemplary embodiment.

FIG. 28 illustrates exemplary graphics software architecture for a dataprocessing system according to exemplary embodiments.

FIG. 29 illustrates a block diagram of an IP core development systemthat may be used to manufacture an integrated circuit (IC) to performoperations according to an exemplary embodiment.

FIG. 30 illustrates a block diagram of an exemplary system on a chip ICthat may be fabricated using or one more IP cores according to anexemplary embodiment.

FIG. 31 illustrates a block diagram of an exemplary graphics processoron a system on a chip IC that may be fabricated using one or more IPcores according to an exemplary embodiment.

FIG. 32 illustrates a block diagram of an exemplary additional graphicsprocessor of a system on a chip IC that may be fabricated using one ormore IP cores according to an exemplary embodiment.

DETAILED DESCRIPTION

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high-speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

In some embodiments, an image capturing device is a standalone device.The image capturing device, however, can be part of or a subcomponent ofanother computing device requiring image capturing capabilities such asa portable or hand-held computing device with a digital camera tocapture images.

In the following description, numerous specific details are set forth toprovide a more thorough understanding. However, it will be apparent thatembodiments described herein may be practiced without one or more ofthese specific details. In other instances, well-known features have notbeen described to avoid obscuring the details of the exemplaryembodiments.

Computing System Overview

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of exemplary embodiments describedherein. The computing system 100 includes a processing subsystem 101having one or more processor(s) 102 and a system memory 104communicating via an interconnection path that may include a memory hub105. The memory hub 105 may be a separate component within a chipsetcomponent or may be integrated within the one or more processor(s) 102.The memory hub 105 couples with an I/O subsystem 111 via a communicationlink 106. The I/O subsystem 111 includes an I/O hub 107 that can enablethe computing system 100 to receive input from one or more inputdevice(s) 108. Additionally, the I/O hub 107 can enable a displaycontroller, which may be included in the one or more processor(s) 102,to provide outputs to one or more display device(s) 110A. In oneembodiment, the one or more display device(s) 110A coupled with the I/Ohub 107 can include a local, internal, or embedded display device.

In one embodiment, the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment,the one or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that an include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment, the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O Hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s), 112 memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment, at least a portion of the componentsof the computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1. For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200 according to an exemplaryembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1 according to an exemplary embodiment.

In one embodiment, the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment, the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment, the front end 208couples with a scheduler 210, which is configured to distribute commandsor other work items to a processing cluster array 212. In oneembodiment, the scheduler 210 ensures that the processing cluster array212 is properly configured and in a valid state before tasks aredistributed to the processing clusters of the processing cluster array212. In one embodiment, the scheduler 210 is implemented via firmwarelogic executing on a microcontroller. The microcontroller implementedscheduler 210 is configurable to perform complex scheduling and workdistribution operations at coarse and fine granularity, enabling rapidpreemption and context switching of threads executing on the processingarray 212. In one embodiment, the host software can prove workloads forscheduling on the processing array 212 via one of multiple graphicsprocessing doorbells. The workloads can then be automaticallydistributed across the processing array 212 by the scheduler 210 logicwithin the scheduler microcontroller.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212. In one embodiment, different clusters 214A-214N of the processingcluster array 212 can be allocated for processing different types ofprograms or for performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment, theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment, the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 214A-214N of the processing cluster array 212. In someembodiments, portions of the processing cluster array 212 can beconfigured to perform different types of processing. For example, afirst portion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 214A-214N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 214A-214N for furtherprocessing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation, the number of partition units 220A-220N isconfigured to be equal to the number of memory units, such that a firstpartition unit 220A has a corresponding first memory unit 224A, a secondpartition unit 220B has a corresponding memory unit 224B, and an Nthpartition unit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the clusters 214A-214N of the processingcluster array 212 can process data that will be written to any of thememory units 224A-224N within parallel processor memory 222. The memorycrossbar 216 can be configured to transfer the output of each cluster214A-214N to any partition unit 220A-220N or to another cluster214A-214N, which can perform additional processing operations on theoutput. Each cluster 214A-214N can communicate with the memory interface218 through the memory crossbar 216 to read from or write to variousexternal memory devices. In one embodiment, the memory crossbar 216 hasa connection to the memory interface 218 to communicate with the I/Ounit 204, as well as a connection to a local instance of the parallelprocessor memory 222, enabling the processing units within the differentprocessing clusters 214A-214N to communicate with system memory or othermemory that is not local to the parallel processing unit 202. In oneembodiment, the memory crossbar 216 can use virtual channels to separatetraffic streams between the clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample, and in one embodiment, some instances of the parallelprocessing unit 202 can include higher precision floating point unitsrelative to other instances. Systems incorporating one or more instancesof the parallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220 according to anexemplary embodiment. In one embodiment, the partition unit 220 is aninstance of one of the partition units 220A-220N of FIG. 2A. Asillustrated, the partition unit 220 includes an L2 cache 221, a framebuffer interface 225, and a ROP 226 (raster operations unit). The L2cache 221 is a read/write cache that is configured to perform load andstore operations received from the memory crossbar 216 and ROP 226. Readmisses and urgent write-back requests are output by L2 cache 221 toframe buffer interface 225 for processing. Updates can also be sent tothe frame buffer via the frame buffer interface 225 for processing. Inone embodiment, the frame buffer interface 225 interfaces with one ofthe memory units in parallel processor memory, such as the memory units224A-224N of FIG. 2 (e.g., within parallel processor memory 222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments, the ROP 226 includes compression logic tocompress depth or color data that is written to memory and decompressdepth or color data that is read from memory. The compression logic canbe lossless compression logic that makes use of one or more of multiplecompression algorithms. The type of compression that is performed by theROP 226 can vary based on the statistical characteristics of the data tobe compressed. For example, in one embodiment, delta color compressionis performed on depth and color data on a per-tile basis.

In some embodiments, the ROP 226 is included within each processingcluster (e.g., cluster 214A-214N of FIG. 2) instead of within thepartition unit 220. In such embodiment, read and write requests forpixel data are transmitted over the memory crossbar 216 instead of pixelfragment data. The processed graphics data may be displayed on a displaydevice, such as one of the one or more display device(s) 110 of FIG. 1,routed for further processing by the processor(s) 102, or routed forfurther processing by one of the processing entities within the parallelprocessor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit according to an exemplary embodiment. In one embodiment,the processing cluster is an instance of one of the processing clusters214A-214N of FIG. 2. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2 and manages execution of those instructions viaa graphics multiprocessor 234 and/or a texture unit 236. The illustratedgraphics multiprocessor 234 is an exemplary instance of a SIMT parallelprocessor. However, various types of SIMT parallel processors ofdiffering architectures may be included within the processing cluster214. One or more instances of the graphics multiprocessor 234 can beincluded within a processing cluster 214. The graphics multiprocessor234 can process data and a data crossbar 240 can be used to distributethe processed data to one of multiple possible destinations, includingother shader units. The pipeline manager 232 can facilitate thedistribution of processed data by specifying destinations for processeddata to be distributed vis the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic supports a variety of operations including integer andfloating point arithmetic, comparison operations, Boolean operations,bit-shifting, and computation of various algebraic functions. In oneembodiment, the same functional-unit hardware can be leveraged toperform different operations and any combination of functional units maybe present.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234,processing can be performed over consecutive clock cycles. In oneembodiment, multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment, the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 308) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 308.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile (talk more about tiling) andoptionally a cache line index. The MMU 245 may include addresstranslation lookaside buffers (TLB) or caches that may reside within thegraphics multiprocessor 234 or the L1 cache or processing cluster 214.The physical address is processed to distribute surface data accesslocality to allow efficient request interleaving among partition units.The cache line index may be used to determine whether a request for acache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234 according to one exemplaryembodiment. In such embodiment, the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 324. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 324. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 324.

The GPGPU cores 262 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 324. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example, and in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment, the FPUs can implement the IEEE 754-2008 standard forfloating point arithmetic or enable variable precision floating pointarithmetic. The graphics multiprocessor 324 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

In one embodiment, the GPGPU cores 262 include SIMD logic capable ofperforming a single instruction on multiple sets of data. In oneembodiment GPGPU cores 262 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. The SIMD instructions for the GPGPU cores can be generatedat compile time by a shader compiler or automatically generated whenexecuting programs written and compiled for single program multiple data(SPMD) or SIMT architectures. Multiple threads of a program configuredfor the SIMT execution model can executed via a single SIMD instruction.For example, and in one embodiment, eight SIMT threads that perform thesame or similar operations can be executed in parallel via a singleSIMD8 logic unit.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 324to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIGS. 3A-3B illustrate additional graphics multiprocessors according toexemplary embodiments. The illustrated graphics multiprocessors 325, 350are variants of the graphics multiprocessor 234 of FIG. 2C. Theillustrated graphics multiprocessors 325, 350 can be configured as astreaming multiprocessor (SM) capable of simultaneous execution of alarge number of execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalexemplary embodiment. The graphics multiprocessor 325 includes multipleadditional instances of execution resource units relative to thegraphics multiprocessor 234 of FIG. 2D. For example, the graphicsmultiprocessor 325 can include multiple instances of the instructionunit 332A-332B, register file 334A-334B, and texture unit(s) 344A-344B.The graphics multiprocessor 325 also includes multiple sets of graphicsor compute execution units (e.g., GPGPU core 336A-336B, GPGPU core337A-337B, GPGPU core 338A-338B) and multiple sets of load/store units340A-340B. In one embodiment, the execution resource units have a commoninstruction cache 330, texture and/or data cache memory 342, and sharedmemory 346.

The various components can communicate via an interconnect fabric 327.In one embodiment, the interconnect fabric 327 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 325. In one embodiment, the interconnectfabric 327 is a separate, high-speed network fabric layer upon whicheach component of the graphics multiprocessor 325 is stacked. Thecomponents of the graphics multiprocessor 325 communicate with remotecomponents via the interconnect fabric 327. For example, the GPGPU cores336A-336B, 337A-337B, and 3378A-338B can each communicate with sharedmemory 346 via the interconnect fabric 327. The interconnect fabric 327can arbitrate communication within the graphics multiprocessor 325 toensure a fair bandwidth allocation between components.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalexemplary embodiment. The graphics processor includes multiple sets ofexecution resources 356A-356D, where each set of execution resourceincludes multiple instruction units, register files, GPGPU cores, andload store units, as illustrated in FIG. 2D and FIG. 3A. The executionresources 356A-356D can work in concert with texture unit(s) 360A-360Dfor texture operations, while sharing an instruction cache 354, andshared memory 362. In one embodiment, the execution resources 356A-356Dcan share an instruction cache 354 and shared memory 362, as well asmultiple instances of a texture and/or data cache memory 358A-358B. Thevarious components can communicate via an interconnect fabric 352similar to the interconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments, which are exemplary. Thus,the techniques described herein may be implemented on any properlyconfigured processing unit, including, without limitation, one or moremobile application processors, one or more desktop or server centralprocessing units (CPUs) including multi-core CPUs, one or more parallelprocessing units, such as the parallel processing unit 202 of FIG. 2, aswell as one or more graphics processors or special purpose processingunits, without departure from the scope of the embodiments describedherein.

In some embodiments, a parallel processor or GPGPU as described hereinis communicatively coupled to host/processor cores to accelerategraphics operations, machine-learning operations, pattern analysisoperations, and various general purpose GPU (GPGPU) functions. The GPUmay be communicatively coupled to the host processor/cores over a bus orother interconnect (e.g., a high speed interconnect such as PCIe orNVLink). In other embodiments, the GPU may be integrated on the samepackage or chip as the cores and communicatively coupled to the coresover an internal processor bus/interconnect (i.e., internal to thepackage or chip). Regardless of the manner in which the GPU isconnected, the processor cores may allocate work to the GPU in the formof sequences of commands/instructions contained in a work descriptor.The GPU then uses dedicated circuitry/logic for efficiently processingthese commands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 are communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440-443 (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440-443 support a communication throughput of 4 GB/s, 30 GB/s, 80GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, and in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links 444-445, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440-443. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 433 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicativelycoupled to a processor memory 401-402, via memory interconnects 430-431,respectively, and each GPU 410-413 is communicatively coupled to GPUmemory 420-423 over GPU memory interconnects 450-453, respectively. Thememory interconnects 430-431 and 450-453 may utilize the same ordifferent memory access technologies. By way of example, and notlimitation, the processor memories 401-402 and GPU memories 420-423 maybe volatile memories such as dynamic random access memories (DRAMs)(including stacked DRAMs), Graphics DDR SDRAM (GDDR) (e.g., GDDR5,GDDR6), or High Bandwidth Memory (HBM) and/or may be non-volatilememories such as 3D XPoint or Nano-Ram. In one embodiment, some portionof the memories may be volatile memory and another portion may benon-volatile memory (e.g., using a two-level memory (2LM) hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one exemplary embodiment. The graphics accelerationmodule 446 may include one or more GPU chips integrated on a line cardwhich is coupled to the processor 407 via the high-speed link 440.Alternatively, the graphics acceleration module 446 may be integrated onthe same package or chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 426may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and bit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneimplementation, a cache 438 stores commands and data for efficientaccess by the graphics processing engines 431-432, N. In one embodiment,the data stored in cache 438 and graphics memories 433-434, N is keptcoherent with the core caches 462A-462D, 456 and system memory 411. Asmentioned, this may be accomplished via proxy circuit 425 which takespart in the cache coherency mechanism on behalf of cache 438 andmemories 433-434, N (e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over link 440, biasingtechniques are used to ensure that the data stored in graphics memories433-434, M is data which will be used most frequently by the graphicsprocessing engines 431-432, N and preferably not used by the cores460A-460D (at least not frequently). Similarly, the biasing mechanismattempts to keep data needed by the cores (and preferably not thegraphics processing engines 431-432, N) within the caches 462A-462D, 456of the cores and system memory 411.

FIG. 4C illustrates another exemplary embodiment in which theaccelerator integration circuit 436 is integrated within the processor407. In this embodiment, the graphics processing engines 431-432, Ncommunicate directly over the high-speed link 440 to the acceleratorintegration circuit 436 via interface 437 and interface 435 (which,again, may be utilize any form of bus or interface protocol). Theaccelerator integration circuit 436 may perform the same operations asthose described with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherency bus 462 and caches462A-462D, 426.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from applications 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 446 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engines 431-432, N.It contains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one exemplary embodiment of ashared model. This embodiment includes a hypervisor real address space498 in which a process element list 499 is stored. The hypervisor realaddress space 498 is accessible via a hypervisor 496 which virtualizesthe graphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of acceleratorintegration slice 490 registers 445.

As illustrated in FIG. 4F, one exemplary embodiment of the inventionemploys a unified memory addressable via a common virtual memory addressspace used to access the physical processor memories 401-402 and GPUmemories 420-423. In this implementation, operations executed on theGPUs 410-413 utilize the same virtual/effective memory address space toaccess the processors memories 401-402 and vice versa, therebysimplifying programmability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the processor 405and GPU 410 it is beneficial to ensure that GPU-biased pages are thosewhich are required by the GPU but not the host processor 405 and viceversa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500 according to anexemplary embodiment. In one embodiment, a graphics processor canimplement the illustrated graphics processing pipeline 500. The graphicsprocessor can be included within the parallel processing subsystems asdescribed herein, such as the parallel processor 200 of FIG. 2, which,in one embodiment, is a variant of the parallel processor(s) 112 ofFIG. 1. The various parallel processing systems can implement thegraphics processing pipeline 500 via one or more instances of theparallel processing unit (e.g., parallel processing unit 202 of FIG. 2)as described herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 3) may be configured to perform the functionsof one or more of a vertex processing unit 504, a tessellation controlprocessing unit 508, a tessellation evaluation processing unit 512, ageometry processing unit 516, and a fragment/pixel processing unit 524.The functions of data assembler 502, primitive assemblers 506, 514, 518,tessellation unit 510, rasterizer 522, and raster operations unit 526may also be performed by other processing engines within a processingcluster (e.g., processing cluster 214 of FIG. 3) and a correspondingpartition unit (e.g., partition unit 220A-220N of FIG. 2). The graphicsprocessing pipeline 500 may also be implemented using dedicatedprocessing units for one or more functions. In one embodiment, one ormore portions of the graphics processing pipeline 500 can be performedby parallel processing logic within a general purpose processor (e.g.,CPU). In one embodiment, one or more portions of the graphics processingpipeline 500 can access on-chip memory (e.g., parallel processor memory222 as in FIG. 2) via a memory interface 528, which may be an instanceof the memory interface 218 of FIG. 2.

In one embodiment, the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinate space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 50. The primitive assembler 506 readingsstored vertex attributes as needed and constructs graphics primitivesfor processing by tessellation control processing unit 508. The graphicsprimitives include triangles, line segments, points, patches, and soforth, as supported by various graphics processing applicationprogramming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516. The geometryprocessing unit 516 is a programmable execution unit that executesgeometry shader programs to transform graphics primitives received fromprimitive assembler 514 as specified by the geometry shader programs. Inone embodiment, the geometry processing unit 516 is programmed tosubdivide the graphics primitives into one or more new graphicsprimitives and calculate parameters used to rasterize the new graphicsprimitives.

In some embodiments, the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and output those fragmentsand associated coverage data to the fragment/pixel processing unit 524.The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping shading blending texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities depending on the sampling rate configured for theprocessing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z test, blending, andthe like, and outputs pixel data as processed graphics data to be storedin graphics memory (e.g., parallel processor memory 222 as in FIG. 2,and/or system memory 104 as in FIG. 1, to be displayed on the one ormore display device(s) 110 or for further processing by one of the oneor more processor(s) 102 or parallel processor(s) 112. In someembodiments, the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

Machine Learning Overview

A machine learning algorithm is an algorithm that can learn based on aset of data. Embodiments of machine learning algorithms can be designedto model high-level abstractions within a data set. For example, imagerecognition algorithms can be used to determine which of severalcategories to which a given input belong; regression algorithms canoutput a numerical value given an input; and pattern recognitionalgorithms can be used to generate translated text or perform text tospeech and/or speech recognition.

An exemplary type of machine learning algorithm is a neural network.There are many types of neural networks; a simple type of neural networkis a feedforward network. A feedforward network may be implemented as anacyclic graph in which the nodes are arranged in layers. Typically, afeedforward network topology includes an input layer and an output layerthat are separated by at least one hidden layer. The hidden layertransforms input received by the input layer into a representation thatis useful for generating output in the output layer. The network nodesare fully connected via edges to the nodes in adjacent layers, but thereare no edges between nodes within each layer. Data received at the nodesof an input layer of a feedforward network are propagated (i.e., “fedforward”) to the nodes of the output layer via an activation functionthat calculates the states of the nodes of each successive layer in thenetwork based on coefficients (“weights”) respectively associated witheach of the edges connecting the layers. Depending on the specific modelbeing represented by the algorithm being executed, the output from theneural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particularproblem, the algorithm is trained using a training data set. Training aneural network involves selecting a network topology, using a set oftraining data representing a problem being modeled by the network, andadjusting the weights until the network model performs with a minimalerror for all instances of the training data set. For example, during asupervised learning training process for a neural network, the outputproduced by the network in response to the input representing aninstance in a training data set is compared to the “correct” labeledoutput for that instance, an error signal representing the differencebetween the output and the labeled output is calculated, and the weightsassociated with the connections are adjusted to minimize that error asthe error signal is backward propagated through the layers of thenetwork. The network is considered “trained” when the errors for each ofthe outputs generated from the instances of the training data set areminimized.

The accuracy of a machine learning algorithm can be affectedsignificantly by the quality of the data set used to train thealgorithm. The training process can be computationally intensive and mayrequire a significant amount of time on a conventional general-purposeprocessor. Accordingly, parallel processing hardware is used to trainmany types of machine learning algorithms. This is particularly usefulfor optimizing the training of neural networks, as the computationsperformed in adjusting the coefficients in neural networks lendthemselves naturally to parallel implementations. Specifically, manymachine learning algorithms and software applications have been adaptedto make use of the parallel processing hardware within general-purposegraphics processing devices.

FIG. 6 is a generalized diagram of a machine learning software stack600. A machine learning application 602 can be configured to train aneural network using a training dataset or to use a trained deep neuralnetwork to implement machine intelligence. The machine learningapplication 602 can include training and inference functionality for aneural network and/or specialized software that can be used to train aneural network before deployment. The machine learning application 602can implement any type of machine intelligence including but not limitedto image recognition, mapping and localization, autonomous navigation,speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 602 can beenabled via a machine learning framework 604. The machine learningframework 604 can provide a library of machine learning primitives.Machine learning primitives are basic operations that are commonlyperformed by machine learning algorithms. Without the machine learningframework 604, developers of machine learning algorithms would berequired to create and optimize the main computational logic associatedwith the machine learning algorithm, then re-optimize the computationallogic as new parallel processors are developed. Instead, the machinelearning application can be configured to perform the necessarycomputations using the primitives provided by the machine learningframework 604. Exemplary primitives include tensor convolutions,activation functions, and pooling, which are computational operationsthat are performed while training a convolutional neural network (CNN).The machine learning framework 604 can also provide primitives toimplement basic linear algebra subprograms performed by manymachine-learning algorithms, such as matrix and vector operations.

The machine learning framework 604 can process input data received fromthe machine learning application 602 and generate the appropriate inputto a compute framework 606. The compute framework 606 can abstract theunderlying instructions provided to the GPGPU driver 608 to enable themachine learning framework 604 to take advantage of hardwareacceleration via the GPGPU hardware 610 without requiring the machinelearning framework 604 to have intimate knowledge of the architecture ofthe GPGPU hardware 610. Additionally, the compute framework 606 canenable hardware acceleration for the machine learning framework 604across a variety of types and generations of the GPGPU hardware 610.

GPGPU Machine Learning Acceleration

FIG. 7 illustrates a highly-parallel general-purpose graphics processingunit 700 according to an exemplary embodiment. In one embodiment, thegeneral-purpose processing unit (GPGPU) 700 can be configured to beparticularly efficient in processing the type of computational workloadsassociated with training deep neural networks. Additionally, the GPGPU700 can be linked directly to other instances of the GPGPU to create amulti-GPU cluster to improve training speed for particularly deep neuralnetworks.

The GPGPU 700 includes a host interface 702 to enable a connection witha host processor. In one embodiment, the host interface 702 is a PCIExpress interface. However, the host interface can also be a vendorspecific communications interface or communications fabric. The GPGPU700 receives commands from the host processor and uses a globalscheduler 704 to distribute execution threads associated with thosecommands to a set of compute clusters 706A-H. The compute clusters706A-H share a cache memory 708. The cache memory 708 can serve as ahigher-level cache for cache memories within the compute clusters706A-H.

The GPGPU 700 includes memory 714A-B coupled with the compute clusters706A-H via a set of memory controllers 712A-B. In various embodiments,the memory 714A-B can include various types of memory devices includingdynamic random access memory (DRAM) or graphics random access memory,such as synchronous graphics random access memory (SGRAM), includinggraphics double data rate (GDDR) memory. In one embodiment, the memoryunits 224A-N may also include 3D stacked memory, including but notlimited to high bandwidth memory (HBM).

In one embodiment, each compute cluster 706A-H includes a set ofgraphics multiprocessors, such as the graphics multiprocessor 400 ofFIG. 4A. The graphics multiprocessors of the compute cluster multipletypes of integer and floating point logic units that can performcomputational operations at a range of precisions including suited formachine learning computations. For example, and in one embodiment, atleast a subset of the floating-point units in each of the computeclusters 706A-H can be configured to perform 16-bit or 32-bit floatingpoint operations, while a different subset of floating point units canbe configured to perform 64-bit floating point operations.

Multiple instances of the GPGPU 700 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment, the multiple instances of the GPGPU 700 communicate over thehost interface 702. In one embodiment, the GPGPU 700 includes an I/O hub708 that couples the GPGPU 700 with a GPU link 710 that enables a directconnection to other instances of the GPGPU. In one embodiment, the GPUlink 710 is coupled to a dedicated GPU-to-GPU bridge that enablescommunication and synchronization between multiple instances of theGPGPU 700. In one embodiment, the GPU link 710 couples with a high speedinterconnect to transmit and receive data to other GPGPUs or parallelprocessors. In one embodiment, the multiple instances of the GPGPU 700are located in separate data processing systems and communicate via anetwork device that is accessible via the host interface 702. In oneembodiment, the GPU link 710 can be configured to enable a connection toa host processor in addition to or as an alternative to the hostinterface 702.

While the illustrated configuration of the GPGPU 700 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 700 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration, the GPGPU 700 includes fewer of the computeclusters 706A-H relative to the training configuration. Additionally,memory technology associated with the memory 714A-B may differ betweeninferencing and training configurations. In one embodiment, theinferencing configuration of the GPGPU 700 can support inferencingspecific instructions. For example, an inferencing configuration canprovide support for one or more 8-bit integer dot product instructions,which are commonly used during inferencing operations for deployedneural networks.

FIG. 8 illustrates a multi-GPU computing system 800 according to anexemplary embodiment. The multi-GPU computing system 800 can include aprocessor 802 coupled to multiple GPGPUs 806A-D via a host interfaceswitch 804. The host interface switch 804, in one embodiment, is a PCIexpress switch device that couples the processor 802 to a PCI expressbus over which the processor 802 can communicate with the set of GPGPUs806A-D. Each of the multiple GPGPUs 806A-D can be an instance of theGPGPU 700 of FIG. 7. The GPGPUs 806A-D can interconnect via a set ofhigh-speed point to point GPU to GPU links 816. The high-speed GPU toGPU links can connect to each of the GPGPUs 806A-D via a dedicated GPUlink, such as the GPU link 710 as in FIG. 7. The P2P GPU links 816enable direct communication between each of the GPGPUs 806A-D withoutrequiring communication over the host interface bus to which theprocessor 802 is connected. With GPU-to-GPU traffic directed to the P2PGPU links, the host interface bus remains available for system memoryaccess or to communicate with other instances of the multi-GPU computingsystem 800, for example, via one or more network devices. While in theillustrated embodiment the GPGPUs 806A-D connect to the processor 802via the host interface switch 804, in one embodiment the processor 802includes direct support for the P2P GPU links 816 and can connectdirectly to the GPGPUs 806A-D.

Machine Learning Neural Network Implementations

The computing architecture provided by embodiments described herein canbe configured to perform the types of parallel processing that isparticularly suited for training and deploying neural networks formachine learning. A neural network can be generalized as a network offunctions having a graph relationship. As is well-known in the art,there are a variety of types of neural network implementations used inmachine learning. One exemplary type of neural network is thefeedforward network, as previously described.

A second exemplary type of neural network is the Convolutional NeuralNetwork (CNN). A CNN is a specialized feedforward neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for computer vision and imagerecognition applications, but they also may be used for other types ofpattern recognition such as speech and language processing. The nodes inthe CNN input layer are organized into a set of “filters” (featuredetectors inspired by the receptive fields found in the retina), and theoutput of each set of filters is propagated to nodes in successivelayers of the network. The computations for a CNN include applying theconvolution mathematical operation to each filter to produce the outputof that filter. Convolution is a specialized kind of mathematicaloperation performed by two functions to produce a third function that isa modified version of one of the two original functions. Inconvolutional network terminology, the first function to the convolutioncan be referred to as the input, while the second function can bereferred to as the convolution kernel. The output may be referred to asthe feature map. For example, the input to a convolution layer can be amultidimensional array of data that defines the various color componentsof an input image. The convolution kernel can be a multidimensionalarray of parameters, where the parameters are adapted by the trainingprocess for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neuralnetworks that include feedback connections between layers. RNNs enablemodeling of sequential data by sharing parameter data across differentparts of the neural network. The architecture for a RNN includes cycles.The cycles represent the influence of a present value of a variable onits own value at a future time, as at least a portion of the output datafrom the RNN is used as feedback for processing subsequent input in asequence. This feature makes RNNs particularly useful for languageprocessing due to the variable nature in which language data can becomposed.

The figures described below present exemplary feedforward, CNN, and RNNnetworks, as well as describe a general process for respectivelytraining and deploying each of those types of networks. It will beunderstood that these descriptions are exemplary and non-limiting as toany specific embodiment described herein and the concepts illustratedcan be applied generally to deep neural networks and machine learningtechniques in general.

The exemplary neural networks described above can be used to performdeep learning. Deep learning is machine learning using deep neuralnetworks. The deep neural networks used in deep learning are artificialneural networks composed of multiple hidden layers, as opposed toshallow neural networks that include only a single hidden layer. Deeperneural networks are generally more computationally intensive to train.However, the additional hidden layers of the network enable multisteppattern recognition that results in reduced output error relative toshallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-endnetwork to perform feature recognition coupled to a back-end networkwhich represents a mathematical model that can perform operations (e.g.,object classification, speech recognition, etc.) based on the featurerepresentation provided to the model. Deep learning enables machinelearning to be performed without requiring hand crafted featureengineering to be performed for the model. Instead, deep neural networkscan learn features based on statistical structure or correlation withinthe input data. The learned features can be provided to a mathematicalmodel that can map detected features to an output. The mathematicalmodel used by the network is generally specialized for the specific taskto be performed, and different models will be used to perform differenttask.

Once the neural network is structured, a learning model can be appliedto the network to train the network to perform specific tasks. Thelearning model describes how to adjust the weights within the model toreduce the output error of the network. Backpropagation of errors is acommon method used to train neural networks. An input vector ispresented to the network for processing. The output of the network iscompared to the desired output using a loss function and an error valueis calculated for each of the neurons in the output layer. The errorvalues are then propagated backwards until each neuron has an associatederror value which roughly represents its contribution to the originaloutput. The network can then learn from those errors using an algorithm,such as the stochastic gradient descent algorithm, to update the weightsof the of the neural network.

FIGS. 9A-9B illustrate an exemplary convolutional neural network. FIG.9A illustrates various layers within a CNN. As shown in FIG. 9A, anexemplary CNN used to model image processing can receive input 902describing the red, green, and blue (RGB) components of an input image.The input 902 can be processed by multiple convolutional layers (e.g.,convolutional layer 904, convolutional layer 906). The output from themultiple convolutional layers may optionally be processed by a set offully connected layers 908. Neurons in a fully connected layer have fullconnections to all activations in the previous layer, as previouslydescribed for a feedforward network. The output from the fully connectedlayers 908 can be used to generate an output result from the network.The activations within the fully connected layers 908 can be computedusing matrix multiplication instead of convolution. Not all CNNimplementations make use of fully connected layers 906. For example, insome implementations, the convolutional layer 906 can generate outputfor the CNN.

The convolutional layers are sparsely connected, which differs fromtraditional neural network configuration found in the fully connectedlayers 908. Traditional neural network layers are fully connected, suchthat every output unit interacts with every input unit. However, theconvolutional layers are sparsely connected because the output of theconvolution of a field is input (instead of the respective state valueof each of the nodes in the field) to the nodes of the subsequent layer,as illustrated. The kernels associated with the convolutional layersperform convolution operations, the output of which is sent to the nextlayer. The dimensionality reduction performed within the convolutionallayers is one aspect that enables the CNN to scale to process largeimages.

FIG. 9B illustrates exemplary computation stages within a convolutionallayer of a CNN. Input to a convolutional layer 912 of a CNN can beprocessed in three stages of a convolutional layer 914. The three stagescan include a convolution stage 916, a detector stage 918, and a poolingstage 920. The convolution layer 914 can then output data to asuccessive convolutional layer. The final convolutional layer of thenetwork can generate output feature map data or provide input to a fullyconnected layer, for example, to generate a classification value for theinput to the CNN.

In the convolution stage 916, the convolutional layer 914 can performseveral convolutions in parallel to produce a set of linear activations.The convolution stage 916 can include an affine transformation, which isany transformation that can be specified as a linear transformation plusa translation. Affine transformations include rotations, translations,scaling, and combinations of these transformations. The convolutionstage computes the output of functions (e.g., neurons) that areconnected to specific regions in the input, which can be determined asthe local region associated with the neuron. The neurons compute a dotproduct between the weights of the neurons and the region in the localinput to which the neurons are connected. The output from theconvolution stage 916 defines a set of linear activations that areprocessed by successive stages of the convolutional layer 914.

The linear activations can be processed by a detector stage 918. In thedetector stage 918, each linear activation is processed by a non-linearactivation function. The non-linear activation function increases thenonlinear properties of the overall network without affecting thereceptive fields of the convolution layer. Several types of non-linearactivation functions may be used. One particular type is the rectifiedlinear unit (ReLU), which uses an activation function defined asƒ(x)=max(0,x), such that the activation is threshold at zero.

The pooling stage 920 uses a pooling function that replaces the outputof the convolutional layer 906 with a summary statistic of the nearbyoutputs. The pooling function can be used to introduce translationinvariance into the neural network, such that small translations to theinput do not change the pooled outputs. Invariance to local translationcan be useful in scenarios where the presence of a feature in the inputdata is more important than the precise location of the feature. Varioustypes of pooling functions can be used during the pooling stage 920,including max pooling, average pooling, and l2-norm pooling.Additionally, some CNN implementations do not include a pooling stage.Instead, such implementations substitute and additional convolutionstage having an increased stride relative to previous convolutionstages.

The output from the convolutional layer 914 can then be processed by thenext layer 922. The next layer 922 can be an additional convolutionallayer or one of the fully connected layers 908. For example, the firstconvolutional layer 904 of FIG. 9A can output to the secondconvolutional layer 906, while the second convolutional layer can outputto a first layer of the fully connected layers 908.

FIG. 10 illustrates an exemplary recurrent neural network 1000. In arecurrent neural network (RNN), the previous state of the networkinfluences the output of the current state of the network. RNNs can bebuilt in a variety of ways using a variety of functions. The use of RNNsgenerally revolves around using mathematical models to predict thefuture based on a prior sequence of inputs. For example, an RNN may beused to perform statistical language modeling to predict an upcomingword given a previous sequence of words. The illustrated RNN 1000 can bedescribed has having an input layer 1002 that receives an input vector,hidden layers 1004 to implement a recurrent function, a feedbackmechanism 1005 to enable a ‘memory’ of previous states, and an outputlayer 1006 to output a result. The RNN 1000 operates based ontime-steps. The state of the RNN at a given time step is influencedbased on the previous time step via the feedback mechanism 1005. For agiven time step, the state of the hidden layers 1004 is defined by theprevious state and the input at the current time step. An initial input(x₁) at a first time step can be processed by the hidden layer 1004. Asecond input (x₂) can be processed by the hidden layer 1004 using stateinformation that is determined during the processing of the initialinput (x₁). A given state can be computed as s_(t)=ƒ(Ux_(t)+Ws_(t-1)),where U and W are parameter matrices. The function ƒ is generally anonlinearity, such as the hyperbolic tangent function (Tan h) or avariant of the rectifier function ƒ(x)=max(0,x). However, the specificmathematical function used in the hidden layers 1004 can vary dependingon the specific implementation details of the RNN 1000.

In addition to the basic CNN and RNN networks described, variations onthose networks may be enabled. One example RNN variant is the long shortterm memory (LSTM) RNN. LSTM RNNs are capable of learning long-termdependencies that may be necessary for processing longer sequences oflanguage. A variant on the CNN is a convolutional deep belief network,which has a structure similar to a CNN and is trained in a mannersimilar to a deep belief network. A deep belief network (DBN) is agenerative neural network that is composed of multiple layers ofstochastic (random) variables. DBNs can be trained layer-by-layer usinggreedy unsupervised learning. The learned weights of the DBN can then beused to provide pre-train neural networks by determining an optimalinitial set of weights for the neural network.

FIG. 11 illustrates an exemplary training and deployment of a deepneural network. Once a given network has been structured for a task theneural network is trained using a training dataset 1102. Varioustraining frameworks 1104 have been developed to enable hardwareacceleration of the training process. For example, the machine learningframework 604 of FIG. 6 may be configured as a training framework 604.The training framework 604 can hook into an untrained neural network1106 and enable the untrained neural net to be trained using theparallel processing resources described herein to generate a trainedneural net 1108.

To start the training process the initial weights may be chosen randomlyor by pre-training using a deep belief network. The training cycle thenbe performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performedas a mediated operation, such as when the training dataset 1102 includesinput paired with the desired output for the input, or where thetraining dataset includes input having known output and the output ofthe neural network is manually graded. The network processes the inputsand compares the resulting outputs against a set of expected or desiredoutputs. Errors are then propagated back through the system. Thetraining framework 1104 can adjust to adjust the weights that controlthe untrained neural network 1106. The training framework 1104 canprovide tools to monitor how well the untrained neural network 1106 isconverging towards a model suitable to generating correct answers basedon known input data. The training process occurs repeatedly as theweights of the network are adjusted to refine the output generated bythe neural network. The training process can continue until the neuralnetwork reaches a statistically desired accuracy associated with atrained neural net 1108. The trained neural network 1108 can then bedeployed to implement any number of machine learning operations.

Unsupervised learning is a learning method in which the network attemptsto train itself using unlabeled data. Thus, for unsupervised learningthe training dataset 1102 will include input data without any associatedoutput data. The untrained neural network 1106 can learn groupingswithin the unlabeled input and can determine how individual inputs arerelated to the overall dataset. Unsupervised training can be used togenerate a self-organizing map, which is a type of trained neuralnetwork 1107 capable of performing operations useful in reducing thedimensionality of data. Unsupervised training can also be used toperform anomaly detection, which allows the identification of datapoints in an input dataset that deviate from the normal patterns of thedata.

Variations on supervised and unsupervised training may also be employed.Semi-supervised learning is a technique in which in the training dataset1102 includes a mix of labeled and unlabeled data of the samedistribution. Incremental learning is a variant of supervised learningin which input data is continuously used to further train the model.Incremental learning enables the trained neural network 1108 to adapt tothe new data 1112 without forgetting the knowledge instilled within thenetwork during initial training.

Whether supervised or unsupervised, the training process forparticularly deep neural networks may be too computationally intensivefor a single compute node. Instead of using a single compute node, adistributed network of computational nodes can be used to accelerate thetraining process.

FIG. 12 is an exemplary block diagram illustrating distributed learning.Distributed learning is a training model that uses multiple distributedcomputing nodes to perform supervised or unsupervised training of aneural network. The distributed computational nodes can each include oneor more host processors and one or more of the general-purposeprocessing nodes, such as the highly-parallel general-purpose graphicsprocessing unit 700 as in FIG. 7. As illustrated, distributed learningcan be performed model parallelism 1202, data parallelism 1204, or acombination of model and data parallelism 1204.

In model parallelism 1202, different computational nodes in adistributed system can perform training computations for different partsof a single network. For example, each layer of a neural network can betrained by a different processing node of the distributed system. Thebenefits of model parallelism include the ability to scale toparticularly large models. Splitting the computations associated withdifferent layers of the neural network enables the training of verylarge neural networks in which the weights of all layers would not fitinto the memory of a single computational node. In some instances, modelparallelism can be particularly useful in performing unsupervisedtraining of large neural networks.

In data parallelism 1204, the different nodes of the distributed networkhave a complete instance of the model and each node receives a differentportion of the data. The results from the different nodes are thencombined. While different approaches to data parallelism are possible,data parallel training approaches all require a technique of combiningresults and synchronizing the model parameters between each node.Exemplary approaches to combining data include parameter averaging andupdate based data parallelism. Parameter averaging trains each node on asubset of the training data and sets the global parameters (e.g.,weights, biases) to the average of the parameters from each node.Parameter averaging uses a central parameter server that maintains theparameter data. Update based data parallelism is similar to parameteraveraging except that instead of transferring parameters from the nodesto the parameter server, the updates to the model are transferred.Additionally, update based data parallelism can be performed in adecentralized manner, where the updates are compressed and transferredbetween nodes.

Combined model and data parallelism 1206 can be implemented, forexample, in a distributed system in which each computational nodeincludes multiple GPUs. Each node can have a complete instance of themodel with separate GPUs within each node are used to train differentportions of the model.

Distributed training has increased overhead relative to training on asingle machine. However, the parallel processors and GPGPUs describedherein can each implement various techniques to reduce the overhead ofdistributed training, including techniques to enable high bandwidthGPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technologicalproblems, including but not limited to computer vision, autonomousdriving and navigation, speech recognition, and language processing.Computer vision has traditionally been one of the most active researchareas for machine learning applications. Applications of computer visionrange from reproducing human visual abilities, such as recognizingfaces, to creating new categories of visual abilities. For example,computer vision applications can be configured to recognize sound wavesfrom the vibrations induced in objects visible in a video. Parallelprocessor accelerated machine learning enables computer visionapplications to be trained using significantly larger training datasetthan previously feasible and enables inferencing systems to be deployedusing low power parallel processors.

Parallel processor accelerated machine learning has autonomous drivingapplications including lane and road sign recognition, obstacleavoidance, navigation, and driving control. Accelerated machine learningtechniques can be used to train driving models based on datasets thatdefine the appropriate responses to specific training input. Theparallel processors described herein can enable rapid training of theincreasingly complex neural networks used for autonomous drivingsolutions and enables the deployment of low power inferencing processorsin a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machinelearning approaches to automatic speech recognition (ASR). ASR includesthe creation of a function that computes the most probable linguisticsequence given an input acoustic sequence. Accelerated machine learningusing deep neural networks have enabled the replacement of the hiddenMarkov models (HMMs) and Gaussian mixture models (GMMs) previously usedfor ASR.

Parallel processor accelerated machine learning can also be used toaccelerate natural language processing. Automatic learning procedurescan make use of statistical inference algorithms to produce models thatare robust to erroneous or unfamiliar input. Exemplary natural languageprocessor applications include automatic machine translation betweenhuman languages.

The parallel processing platforms used for machine learning can bedivided into training platforms and deployment platforms. Trainingplatforms are generally highly parallel and include optimizations toaccelerate multi-GPU single node training and multi-node, multi-GPUtraining. Exemplary parallel processors suited for training include thehighly-parallel general-purpose graphics processing unit 700 of FIG. 700and the multi-GPU computing system 800 of FIG. 800. On the contrary,deployed machine learning platforms generally include lower powerparallel processors suitable for use in products such as cameras,autonomous robots, and autonomous vehicles.

FIG. 13 illustrates an exemplary inferencing system on a chip (SOC) 1300suitable for performing inferencing using a trained model. The SOC 1300can integrate processing components including a media processor 1302, avision processor 1304, a GPGPU 1306 and a multi-core processor 1308. TheSOC 1300 can additionally include on-chip memory 1305 that can enable ashared on-chip data pool that is accessible by each of the processingcomponents. The processing components can be optimized for low poweroperation to enable deployment to a variety of machine learningplatforms, including autonomous vehicles and autonomous robots. Forexample, one implementation of the SOC 1300 can be used as a portion ofthe main control system for an autonomous vehicle. Where the SOC 1300 isconfigured for use in autonomous vehicles the SOC is designed andconfigured for compliance with the relevant functional safety standardsof the deployment jurisdiction.

During operation, the media processor 1302 and vision processor 1304 canwork in concert to accelerate computer vision operations. The mediaprocessor 1302 can enable low latency decode of multiple high-resolution(e.g., 4K, 8K) video streams. The decoded video streams can be writtento a buffer in the on-chip-memory 1305. The vision processor 1304 canthen parse the decoded video and perform preliminary processingoperations on the frames of the decoded video in preparation ofprocessing the frames using a trained image recognition model. Forexample, the vision processor 1304 can accelerate convolution operationsfor a CNN that is used to perform image recognition on thehigh-resolution video data, while back end model computations areperformed by the GPGPU 1306.

The multi-core processor 1308 can include control logic to assist withsequencing and synchronization of data transfers and shared memoryoperations performed by the media processor 1302 and the visionprocessor 1304. The multi-core processor 1308 can also function as anapplication processor to execute software applications that can make useof the inferencing compute capability of the GPGPU 1306. For example, atleast a portion of the navigation and driving logic can be implementedin software executing on the multi-core processor 1308. Such softwarecan directly issue computational workloads to the GPGPU 1306 or thecomputational workloads can be issued to the multi-core processor 1308,which can offload at least a portion of those operations to the GPGPU1306.

The GPGPU 1306 can include compute clusters such as a low powerconfiguration of the compute clusters 706A-706H within thehighly-parallel general-purpose graphics processing unit 700. Thecompute clusters within the GPGPU 1306 can support instruction that arespecifically optimized to perform inferencing computations on a trainedneural network. For example, the GPGPU 1306 can support instructions toperform low precision computations such as 8-bit and 4-bit integervector operations.

FIG. 14 illustrates an exemplary image capturing system for an imagecapturing device. In some embodiments, the image capturing device is astand-alone device such as a digital camera. In other embodiments, theimage capturing device is part of or a subcomponent of another computingdevice requiring image capturing capabilities such as a portable orhand-held computing device with a digital camera to capture images. Inexemplary embodiments, an image capturing device is integrated with aConvolutional Neural Network (CNN) computation processing, such as adeep learning neural network (DNN) that can provide DNN data inreal-time or simultaneously with or at the time of image capturing andstorage by the image capturing device. Thus, the image capturing devicedisclosed herein can provide both image and color information as well asdeep machine learning information such as feature detection using a CNN.

FIG. 14 also illustrates an exemplary diagram of storing RGB and CNNchannel data as part of a deep channel image. The exemplary system isshown having a deep channel camera 1400 and deep channel memory 1420.The deep channel camera 1400 includes a color sensor 1402 and a FPGAunit 1404. Color sensor 1402 includes a plurality of sensors arranged inan array to capture color data for an image 1403A. The image 1403A canbe for an RGB image or a YUV image. Color sensor 1402 outputs imagevalues or data to deep channel memory 1420 and FPGA unit 1404.

The color sensor can be any type of photo-sensor, photo-detector, CMOSsensor, or pixel array having an array of light sensors to detect lightenergy that forms color images captured by the color sensor array.Alternatively, the color sensor can be configured to detect non-colorimages. The data or values for each color component forms a separateoutput channels such as RGB color channels 1414.

In one embodiment, the outputs of color sensor array 1402 are inputs toa neural network 1406 programmed in FPGA 1404. The network can be anytype of network with any number of convolutional layers and nodes ateach layer that can perform neural network processing as disclosed inexemplary embodiments regarding FIGS. 9A and 9B. In other examples,hardwired circuitry or a programmable logic arrays (PLAs) can be used inplace of FPGA 1406 with the neural network programmed or hardwired inrespective circuitry or arrays in the same way. In other embodiments,FPGA 1404 can be programmed with hardware acceleration as described inFIG. 6 regarding machine learning application 602, machine learningframework 604, and compute frame work 606.

In one embodiment, FPGA unit 1404 is programmed with a standard verydeep CNN such as the Visual Geometry Group (VGG) CNN labeled as standardVGG network 1406. In other embodiments, other types of CNN, such as aDNN, can be programmed in FPGA unit 1404 as disclosed herein. In thisexample, VGG network 1406 is programmed with five (5) convolution layerslabeled as “Conv1” through “Conv5.” Each convolution layer Conv2 throughConv5 includes a “pool” of data providing a summary statistic of nearbyoutputs. In other embodiments, any number of convolution layers can beused including a single convolution layer. Each Conv1 through Conv5layers performs convolution operations that provide filtered image orfeature map data labeled as “Data of Conv1” through “Data of Conv5.” Inthis feedforward CNN network, the output of each convolution layerspasses to the next layer to a final prediction layer 1425. At theprediction layer 1425, a classification 1430 can be made such asdetermining a type of image based on the CNN processing and CNN data.

The deep channel memory 1420 stores an image 1403B for RGB channels 1414derived from image 1403A. The Data of Conv1 (1410A) through Conv5(1410E) is stored for CNN channels 1418. In this embodiment, eachconvolution channel Conv1 through Conv5 represents a CNN channel havingfive channels for CNN channels 1418. The Data of Conv1 (1410A) throughConv5 (1410E) can be different filtered images or feature maps of image1403A depending on the convolutional processing performed at each layer.In deep channel memory 1420, images for RGB color channels 1414 and CNNchannels are stored in adjacent memory or concatenated together.

Deep Learning with Discriminative Semantic Transfer and Physics-InspiredOptimization

As described in the context of FIGS. 12 and 14, a deep learning networkhas a plurality of nodes across several different domains. Given aninput image, a deep learning based classification model is describedwhich can process particular-domain pixel-wise scene parsing.Specifically, this particular-domain pixel-wise scene parsing (e.g.,predicting pixel-wise wall-to-floor, wall-to-wall and wall-to-ceilingedges, especially in cluttered indoor scenes) is quite different fromconventional-domain scene parsing (e.g., pixel-wise objectclassification or segmentation). Furthermore, the data for training sucha particular task is very limited (only thousand level) and seriouslyunbalanced.

The following examples and embodiments, referring to FIGS. 15 through18G, disclose techniques, which provide good transfer across multipledomains and addresses seriously unbalanced training data with only avery limited number of samples (only thousand level). The disclosedtechniques may be considered in the light of three aspects: (1) aformulation to integrate the relationship between the scene clutter ofone domain and the room layout of another domain, room layout is oneexample, the techniques may be applied to other types of domains) intoConvolutional Neural Networks (CNNs); (2) a lightweight CNN architecturethat can be end-to-end trained with a 3-stage incremental trainingmethod; and (3) a practical strategy to initialize weights for very deepCNNs under a limited and unbalanced training data distribution.

These disclosed techniques are optimized using an inference schemereferred to herein as physics inspired optimization in which room layoutestimation is formulated to naturally integrate phenomena observedfeatured characteristics into mechanics concepts. For example, twotheoretically elegant and practically tractable optimization techniquesare presented, making a tactful balance of efficiency and accuracy, thusdemonstrating leading accuracy with real-time processing speed. Usingthese techniques, room layout estimation can be smartly cast and solvedfrom the perspective of a pure optimization task, in contrast, tomainstream methods primarily resorting to computation-intensive proposaland re-ranking strategies.

The disclosed techniques may be used as a part a room layout estimationsystem that operates with overwhelming margins of speed and accuracycompared with other estimation systems that were designed formachine/deep learning and computer vision for artificial intelligence.The described discriminative semantic transfer techniques can have wideapplications including, but not limited to, image classification,pixel-wise scene parsing, semantic image segmentation, object detection,room layout estimation and robotics localization and tracking. It mayalso be applied to any kind of semantic transfer tasks in the context ofdeep learning or conventional learning methods or beyond.

Introduction

Semantic transfer (ST) has three features providing three differentperspectives: (1) As a discriminative model, it integrates therelationship between room layout and scene clutter into an FCN. (2) Asan architecture, it enjoys the benefits of end-to-end training. 3) As atraining strategy, it provides better network initialization and allowsa very deep network to be trained under unbalanced training datadistribution. Semantic transfer thus provides highly robust featuresunder all kinds of different circumstances.

Semantic transfer and physics-inspired optimization techniques aredisclosed as detailed below with regards to FIGS. 15-18G. Numbers inbrackets refer to named references identified in the References sectionbelow.

Given an input RGB image, a room layout estimation algorithm shouldoutput all the wall-floor, wall-wall, and wall-ceiling edges (e.g.,depicted by FIG. 15). This is a fundamental indoor scene understandingtask as it can provide a strong priori for other tasks like depthrecovery from a single RGB image shown in References [6] and [7] orindoor object pose estimation shown in References [9], [22] and [23].Besides, room layout itself provides a high-level representation of anindoor scene for emerging applications like intelligent robots andaugmented reality. This problem draws constant attention since thepublication of seminal work described in Reference [11], and there aretwo lines of followers:

As the upper part of FIG. 15 shows, conventional methods follow aproposing-ranking scheme. Typically, the proposing part consists ofthree sub-modules as edge detection, vanishing point voting and raysampling. With hand-crafted features and structured inferencetechniques, the ranking part outputs the best layout proposal, sometimesalong with a representation of the clutter.

Recent methods disclosed in References [3], [17] and [27] achievedramatic performance improvements via features produced by fullyconvolutional networks (FCNs). Yet, References [17] and [27] stillfollow the traditional proposing-ranking scheme. Reference [3] is aproposal-free solution in which all those steps about proposalgeneration are eliminated. And instead of proposal ranking, in Reference[3], inference is achieved through an optimization module.

Referring to FIG. 15, above the dash line, the diagram depicts anoverview of conventional methods, and below the dash line, the diagramdepicts an overview of the disclosed techniques using semantic transferand physics inspired optimization.

Conventional Methods.

Conventional methods provide many useful insights about indoor sceneunderstanding. For example, Reference [11] and other references [4],[5], [20], [21], [25] and [29] explore different ways to model therelationship between room layout and scene clutter. This effort isreasonable because the major challenges of room layout estimation liehere. Take for example, referring back to FIG. 15, over 50% ofwall-floor edge pixels is occluded by the bed. If the bed does notexist, this task can be simplified. However, these insights are notaddressed by recent FCN-based room layout estimation works. Forinstance, when designing networks, recent works treat FCNs as blackboxes, taking no scene clutter information into consideration. Asmodelling meaningful concepts with a neural network has always beendifficult, the disclosed techniques explore the possibility to describescene clutter within an FCN.

FCN-Based Methods.

Unlike References [17] and [27], which still follow theproposing-ranking scheme, Reference [3] shows a framework havingintriguing compactness. However, its optimization is primitive in whichthe sampled solution space is exhaustively searched and no gradient ismodeled. As such, the disclosed techniques provide a principled,gradient-based, and efficient optimization algorithm and process fordeep networks.

Disclosed examples and embodiments provide semantic transfer (ST)techniques, which include three features from three differentperspectives: (1) As a discriminative model, the disclosed ST techniquesintegrate the relationship between room layout and scene clutter into anFCN. (2) As an architecture, the disclosed ST techniques enjoy thebenefits of end-to-end training. (3) As a training strategy, thedisclosed ST techniques provide better network initialization and allowstraining a very deep network under unbalanced training datadistribution. The disclosed ST techniques provide highly robust featuresunder all kinds of circumstances.

Additionally, in order to address the computation redundancy hidden inthese features, disclosed examples and embodiments also propose physicsinspired optimization (PIO) techniques. The disclosed ST and PIOtechniques play different, yet closely interdependent roles because thecore idea of PIO is to formulate some phenomena observed in ST featuremaps with mechanics concepts.

Related Work

Conventional Methods.

These methods follow the standard definition of room layout estimation,which was introduced by Reference [11]. Such an estimation clustersedges into lines joining at three vanishing points, according to thefamous Manhattan assumption described in Reference [2]. Many proposalshave also been disclosed based on ray sampling. Handcrafted features areused to learn a regressor for proposal ranking. Other works try toimprove this framework. For instance, Reference [19] detectsconjunctions instead of edges and modifies proposal generation andranking accordingly. While ranking room layouts, Reference [25]simultaneously estimates a clutter mask. Reference [21] aims to improvethe inference efficiency of methods like Reference [25]. Going beyondestimating clutter mask, Reference [20] estimates 3D bounding boxes ofobjects and room layout during inference. Except for learnt clutterrepresentations, Reference [4] incorporates furniture shape priori. InReference [5], furniture is modeled with parts instead of a box.Reference [29] goes even further by modelling furniture relationshipwith scene grammars. These References form a good tradition ofexploiting the relationship between room layout and scene clutter.However, these conventional methods are absent in recent FCN-basedmethods.

FCN-Based Methods.

Recently, Reference [17] describes training an FCN for pixel-wise edgelabelling, with every pixel assigned a label from this 4-class set S{background (bg), wall-floor edge (wf), wall-wall edge (ww),wall-ceiling edge (wc)}.

S={bg,wf,ww,wc}  (1)

Activations of the last layer are incorporated into the conventionalinference framework as features. Reference [3] uses another formulationin which every pixel may be assigned a label from a 5-class set {floor,left wall, middle wall, right wall, ceiling}. This 5-class formulationhas an ambiguity problem as the patterns of three type of walls are notdiscriminative in nature. FCN is coordinate-invariant sinceconvolutional layers actually conduct a sliding window search, so it isnot suitable to tell the difference between left wall and right wall.Thus, Reference [3] uses an additional ambiguity clarification step.Reference [27] uses both formulations for FCN training. These FCN-basedworks show dramatic performance improvements, but their inferenceschemes remain conventional or primitive. With robust FCN features, itis possible to design more principled and efficient inference schemes.

Broader Literature.

Such literature describes other scene understanding tasks substantiallysame as or similar to room layout estimation. For example, Reference[18] tries to understand the layouts of natural scenes with a horizon,urban scenes, corridor and others, for which room layout estimation isonly a special case. Another special case of Reference [18] is outdoorurban layout estimation, such as described References [1] and [13]. Suchestimations are often regarded as a graphics applications under the nameof photo pop-up and evaluated with subjective user study. Reference [14]tries to recover more detailed room layout than a box and evaluates withwall-floor edge error. Since these works exploit techniques thatReference [11] is built upon, these References could potentially benefitfrom examples and embodiments disclosed herein.

Concepts Similar to ST and PIO.

Looking at an even broader literature, the concepts somewhat similar toST and PIO have already been discussed. Under the name of labeltransfer, References [16] and [28] address semantic segmentation in anonparametric manner. ST is different from such literature primarily asa unified deep architecture (and of course in its parametric nature).Reference [8] and its progeny are famous for stating human limbs assprings. PIO is different from such literature primarily as an efficientapproximation inspired by mechanics concepts.

Semantic Transfer

Examples and embodiments of disclosed semantic transfer (ST) techniquesis proposed as shown in FIG. 16. For one embodiment, ST techniques areproposed in 3 stages. For ease understanding, stage four, which is theinference phase, can be viewed as inference phase that is the ultimategoal of FCN—i.e., pixel-wise edge labelling. As demonstrated by stagefour panel in FIG. 16, four pixel-wise activation maps are extractedfrom input image, with each one corresponding to a label from S (set 1).For example, in the wf activation map higher color temperature indicateshigher possibility of wf existence.

Referring to FIG. 16, the Top part depicts probabilistic nodeconnectivity in our formulation, while the bottom part depicts semantictransfer (ST) in three stages. Stage one refers to semantic segmentationpre-training. Stage two refers to classifying edge with pixel-wisesemantic features. Stage three refers to end-to-end edge labelfine-tuning. In stage three, pre-trained network refers to the oneoutlined by the dashed box in stage one. In stage four, pixel-wise edgelabelling network refers to the one outlined by the dashed box in stagethree.

FIG. 17 is a diagram of a network design for semantic segmentation,semantic transfer, semantic feature space, and transfer weightsvisualization according to exemplary embodiments. FIG. 17 is dividedinto four parts (a) through (d). In part (a), Network design for ST instage one is shown. In part (b), Qualitative results for semanticsegmentation on dataset LSUN is shown. In part (c), Unsupervisedstructure visualization of the semantic feature space is shown. In part(d), Transfer weights Visualization is shown. For one embodiment, LSUNdoes not provide semantic segmentation ground truth for quantitativeevaluation. Regarding the different sections of FIG. 17, the Left-Top,Right-Top, Left-Bottom and Right Bottom can be referred to as background(bg), wall-floor edges (wf), wall-wall edges (ww) and wall-ceiling edges(wc).

FIG. 18A is a diagram on the left of feature quality visualization. Fromtop to bottom: input, bg activation, wf activation, ww activation, wcactivation, softmax results. On the right is a feature qualitycomparison. In this example, every feature map is numerically normalizedindependently. For one embodiment, feature maps produced by thesedisclosed techniques can be processed into a style described inReference [17].

For one embodiment, in stage one, an FCN can be trained for 37-classsemantic segmentation on dataset SUNRGBD in order to describe acluttered scene to the utmost extent. These 37 categories can cover mostof the items and furniture that commonly appear in an indoor scene, likewall, ceiling, chair or window. The FCN can be built upon the newlyintroduced architecture ResNet-101 as described in Reference [10].

Referring to part (a) of FIG. 17, net surgeries can be performed to thelast two sets of bottlenecks in original ResNet-101, with the holemechanism described in Reference [15] (under the name of dilatedconvolution in Reference [26]). Inputs to this network (RGB images) areactually random variables X taking values from [0,255]. X is determinedby hidden random variables Y taking values from semantic labels [1,37].Thus this network describes the posterior distribution P(Y|X).

In stage two, the room layout dataset LSUN is fed through the semanticsegmentation network, producing pixel-wise 37-channel semantic features.As indoor scene understanding datasets, the model trained on SUNRGBDgeneralizes well on LSUN. Referring to part (b) of FIG. 17, qualitativeresults on LSUN are shown, all of which are produced by a softmaxoperation without post processing techniques like conditional randomfields. Then treating every pixel as a sample, a fully connected layercan be learned to bridge the gap between 37-channel semantic featuresand 4-class edge labels. In order to illustrate that semantic featuresare discriminative for this task, a standard unsupervised analysis canbe implemented with t-sne as described in Reference [24]. Referring topart (c) in FIG. 17, samples of wall-ceiling edges (wc) are shown andwall-floor edges (wf) form obvious clusters in the embedding space. Yet,some samples of wall-wall edges (ww) and background (bg) scatter amongeach other. In this stage, Y is determined by hidden random variables Ztaking values from edge labels as described in References [1] and [4](set 1). So this fc layer describes the posterior distribution P(Z|Y).

P(Z|Y) is a parameterized representation of the relationship betweenroom layout and scene clutter. Unlike pioneering works, the disclosedtechniques model this relationship directly in a neural network. This isinspired by understanding a room layout. As demonstrated by stage twopanel in FIG. 16, the network in stage one extracts 37-channel semanticfeatures from the scene. Only the channel on top of the stack is fullyillustrated, and that channel corresponds to the window. This channelcan roughly tell the locations and extensions of three windows in thescene. How would a human brain parse room layout from semantic featureslike this? The disclosed examples and embodiments hypothesize decisionsaccording to rules like: wall-floor edges cannot go through windows, sothey are less possible to appear in areas with high window scores.

In order to validate such a hypothesis, for one embodiment, the transferweights can be visualized in this fc layer. These weights can be learntfor 100 times independently and organized into box figure as shown inpart (d) of FIG. 17. Not surprisingly, wall, floor and ceiling channelsof semantic features contribute the most to ww, wf and wc, respectively.Generally speaking, higher scores with smaller boxes means strongercorrelation. Taking wc for example, except for ceiling, top fourtransfer weights come with cabinet, picture, sofa and whiteboard.

According to common sense, cabinet, picture and whiteboard tend toappear in the receptive field of a wc pixel, because they are verticallyhigh in physical space. sofa is usually lower, so its variation(depicted by box size) is twice picture's. whiteboard is rare,explaining why its variation is also large.

In stage three, for one embodiment, the learnt 37×4 fc layer is reshapedinto a 1×1×37×4 convolutional layer and added on top of the networktrained in stage one. For one embodiment, weights from stage one act asa feature extractor, and weights from stage two act as a classifier. Theweights can form a pixel-wise edge labelling network describingP(Z|Y)P(Y|X)=P(Z|X). On one hand, this network can be end-to-endfine-tuned on LSUN for edge labelling, which is the ultimate goal wementioned at the beginning of this section. On the other hand, itincorporates the relationship between scene clutter and room layoutelegantly, which is a factor to the disclosed techniques.

Except for end-to-end training and scene clutter modelling, an advantageof semantic transfer is better initialization for extreme unbalancedtraining data. Training this pixel-wise edge labelling network directlywith the ResNet FCN (part (a) in FIG. 17) can be done leaving out ST.But outputs of the first batch normalization (BN) layer are prone tooverflow, making training fail. Training problems are also reported inSection 3.2 of Reference [17], which describes the network having to bepre-trained on NYUd2 and pre-training on PASCAL, which leads to badresults. This problem may be caused by the extreme unbalanceddistribution of edge labels. As shown in the stage two panel in FIG. 16,over 99% labels are background. Like the classical method ofinitializing auto-encoder with multiple restricted boltzmann machines,see, e.g., Reference [12], the disclosed pixel-wise edge labellingnetwork is initialized by the first two stages such that the overflowphenomenon with ST is no longer observed. For one embodiment,probabilistic nodes' connectivity is illustrated by the upper part ofFIG. 16.

Optimization

For one embodiment, the disclosed pixel-wise edge labelling networkproduces highly robust features in all types of scenes, as visualized bythe left panel in FIG. 18A. The samples can collected from LSUN testset, covering different types of scene clutter, layout configurations,and illumination conditions. For one embodiment, feature qualitycomparisons can be provided against Reference [17] failure cases, asdemonstrated by the right panel in FIG. 18A. The three scenes in FIG.18-1 are challenging because almost all edge pixels are occluded byclutters like sofas, tables or beds. Reference [17] network, which doesnot consider the relationship between room layout and scene clutter,fails to extract reliable edge features, in contrast to the disclosednetwork and techniques which handles them properly.

As shown by FIG. 18A, for one embodiment, on these features it ispossible to estimate room layout directly with image processingtechniques. Using the possibility of applying hough transformation toeroded softmax results, qualitative results can be good. However, usinga series of image processing modules for inference is not a good choice,as many threshold parameters related to erosion or hough transformationhave to be tuned. Instead, optimized parameterized layout can be used.

FIG. 18B is a diagram of a topology of a room layout from a test setaccording to an exemplary embodiment. This diagram can show a 6thtopology clipped from LSUN specification representation with thosefeatures. The disclosed techniques propose two differentimplementations: naive optimization (NO) and physics inspiredoptimization (PIO).

Notation and Formulation

Topology. For one embodiment, there can be 11 different possible roomlayout topologies in a 2D image, as demonstrated in supplementarymaterial. We denote them as T_(i)(i∈[1,11]).

Conjunction. For one embodiment, each topology can be parameterized bythe edge conjunctions set P_(i)={P_(ij),j∈[1,nC]}, with every P_(ij) asa 2D coordinate and nC as the conjunction number.

Edge. For one embodiment, conjunction connectivity can be defined byedge setE_(i)={E_(ik)=(Q_(ka),Q_(kb),c),Q_(ka)∈P_(i),Q_(kb)∈P_(i),c∈S,k∈[1,nE]},with nE as the edge number. S is set 1.

Take the 6th topology as example (FIG. 18B), the parameterizedrepresentation is P₆={P_(6j),j∈

[1,6]} andE₆={E_(6k)=(Q_(ka),Q_(kb),c),Q_(ka)∈P₆,Q_(kb)∈P₆,c∈S,k∈[1,5]}), sincenC=6 and nE=5.

Edge label map. For one embodiment, P, and E, can be converted into apixel-wise edge label map which is similar to the output in FIG. 15.This conversion is denoted as M=C(P_(i),E_(i)) and we will omit E_(i)later because it does not change for a certain topology T_(i). Also,M[P_(i)] can be used when referring the map M produced from conjunctionset P_(i) and M[P_(ij)] when a certain conjunction P_(ij) is underconsideration. C is implemented by assigning corresponding labels from S(set 1) to pixels between two conjunctions.

For one embodiment, the features produced by the pixel-wise edgelabelling network are denoted as F_(l)(l∈[1,4]). For one embodiment,both M and F_(l) are of the same size as input image, denoted by (w,h).For one embodiment, the consistency objective (CO) and its correspondingenergy format (e) can be defined on (w,h):

$\begin{matrix}{{C\; O} = {\frac{1}{wh}{\sum\limits_{l = 1}^{4}{\sum\limits_{m = 1}^{w}{\sum\limits_{n = 1}^{h}{{F_{l}\left( {m,n} \right)} \times {M_{l}\left( {m,n} \right)}}}}}}} & (2) \\{e = {\exp \left( {{- C}\; O} \right)}} & (3)\end{matrix}$

in which M_(l)(l∈[1,4]) is the binary mask generated from M by setting apixel to one if M(m,n)=l and zero otherwise. For every differenttopology T_(i), in one embodiment, the best parameterized representationP_(i) can be found by minimizing e:

$\begin{matrix}{{Pi} = {\underset{Pi}{argmin}\; e}} & (4)\end{matrix}$

In most cases, starting from the right topology leads to the lowestenergy value and wrong topologies lead to higher energy values. Failurecases do exist and we will visualize them later. For one embodiment, theoptimization implementations detailed below are initialized from theaverage state of P_(i) set (such as the one demonstrated by the leftfigure in FIG. 18B).

Naive Optimization

For one embodiment, in solving Equation 4, a possible solution is tomimic the scheme presented in Reference [3] by sampling P_(i) around theinitialization state and exhaustively searching for the one thatgenerates the lowest e. This method in Reference [3] does not model anygradients and the solution space is restrained. By contrast, agradient-based optimization modules can be built that can iterativelyapproach the right layout from average initialization. For oneembodiment, NO can be calculated and implemented as follows:

$\begin{matrix}{\frac{\partial e}{\partial P_{ijx}} \approx {{e\left( {{Pij}\left( {x + {\Delta \; x}} \right)} \right)} - {e\left( {{Pij}\left( {x - {\Delta \; x}} \right)} \right)}}} & (5) \\{\frac{\partial e}{\partial P_{ijy}} \approx {{e\left( {{Pij}\left( {y + {\Delta \; y}} \right)} \right)} - {e\left( {{Pij}\left( {y - {\Delta \; y}} \right)} \right)}}} & (6) \\{{\Delta \; {Pij}} = {\alpha \times \left( {{- \frac{\partial e}{\partial P_{ijx}}},{- \frac{\partial e}{\partial P_{ijy}}}} \right)}} & (7)\end{matrix}$

Algorithm 1 Naive Optimization   Initialize: average P_(i) while edecreases do  for all j do   update P_(ij) according to Equation 5, 6,and 7  end for  calculate e at updated P_(i) end whilein which α is the scaling factor and Δx (=Δy) is the window size. Forconjunctions at image boundary (e.g. P₆₂ in left figure of FIG. 18B), anadditional constraint can be imposed by setting corresponding componentof ΔP_(ij) to zero. For one embodiment, if conjunctions move to imagecorners, ΔP_(ij) is treated as a special case so as to allow theconjunction to move onto another boundary or just stick to the corner.The convergence performance of NO can be good, but it is very slow so weintroduce PIO as an efficient alternative.

Analysis and Motivation

For one embodiment, the efficiency bottleneck of NO can be analyzed.When calculating Equation 5 (and similarly Equation 6),

$\begin{matrix}{\frac{\partial e}{\partial P_{ijx}} \propto {- \left( {{{CO}\left( {{Pij}\left( {x + {\Delta \; x}} \right)} \right)} - {{CO}\left( {{Pij}\left( {x - {\Delta \; x}} \right)} \right)}} \right)}} & (8) \\{= {- {\sum\limits_{l = 1}^{4}{\sum\limits_{m,n}{{Fl} \times \left( {{M_{l}\left\lbrack {{Pij}\left( {x + {\Delta \; x}} \right)} \right\rbrack} - {M_{l}\left\lbrack {{Pij}\left( {x - {\Delta \; x}} \right)} \right\rbrack}} \right)}}}}} & (9)\end{matrix}$

FIG. 18C is a diagram of a room shown in an input image, feature mapconstructed by considering an edge as a spring and the feature map as apotential field and correlating forces imposed on the spring's everypoint, and influence of force composition according to exemplaryembodiments. Referring to FIG. 18C, (ag) M₂[P_(ij(x−Δx))]; (ah)M₂[P_(ij)]; (ai) M₂[P_(ij(x+Δx))] and (b) is the input image. (c/d) Ifwe consider an edge as a spring and the feature map as a potentialfield, forces imposed on the spring's every point are correlated. (e/f)The influence of force composition. In Equation 9, m,n can be omittedwhose meanings are stated in Equation 2. CalculatingM_(l)[P_(ij(x+Δx))]−M_(l)[P_(ij(x−Δx))] is the efficiency bottleneck,which represents M=C(P_(i))'s gradient and intuitively we illustrateM₂[P_(ij(x+Δx))]−M₂[P_(ij(x−Δx))] with part (a) of FIG. 18C (l=2 becausewall-floor edge is 2nd label from S, set 1). It actually subtracts twopixel-wise masks, with M₂[P_(ij(x+Δx))] illustrated by FIG. 18C ai andM₂[P_(ij(x−Δx))] by FIG. 18C ag.

For one embodiment, M is generated by conversion C. For one embodiment,C can be implemented by traversing every pixel to decide its label. Forone embodiment, the scale of w,h can be denoted by N, complexity of thisimplementation (referred as NOA later) is O(N²) for every calculation ofEquation 5 or 6, which can run for tens of minutes for an image.

For one embodiment, an improved implementation (referred as NOB later)of C calculates pixel coordinates between two conjunctions and accessescorresponding mask element directly. Its complexity is O(N), and it runsfor about 30 s for an image. The idea of further reducing the complexityto O(1) motivates us to introduce PIO.

For one embodiment, every edge can be considered as a spring, which maytranslate, rotate and change its length. In NO, edges' movements aredecided by every pixel on them, yet there is computation redundancy. Asdemonstrated by parts (c) and (d) in FIG. 18C, the feature map can be asa potential field to analyze how points on the edge move. Notsurprisingly, their movements are not independent and can be roughlyinterpolated from the movements of the edge's two endpoints, that isQ_(ka) and Q_(kb). For one embodiment, based on this observation,ΔP_(ij) can be approximated with gradients defined on P_(i) instead ofM_(l)[P_(i)]. Since the number nC of conjunctions P_(i) is constant, thecomplexity is O(1). This is PIO's first key concept.

Force composition is the second key concept of PIO. As demonstrated bypart (e) in FIG. 18C, considering the endpoints of edge j and k insteadof every points on them they will move towards a local minima state.This will be corrected by calculating the movements of edge l (see part(f) in FIG. 18C), in which another feature map (wall-wall edge) will beused as the potential field. So the movement of every conjunction shouldbe decided by the forces imposed on every edge that is connected to thatconjunction. Obviously adding two gradient vectors (such as the ones inparts (e) and (f) FIG. 18C and FIG. 18C) naturally obeys theparallelogram law of force composition.

Physics Inspired Optimization

For the first concept, a new consistency objective can be defined foreach endpoint of an edge E_(ik)=(Q_(ka),Q_(kb),c):

CO2=Fc(Qkax,Qkay)  (10)

e2=exp(−CO2)  (11)

-   -   As a reminder, the meanings of E and F are stated at the        beginning of this section. Calculating the gradient of a certain        point in a potential field is trivial as:

$\begin{matrix}{\frac{{\partial e}\; 2}{\partial Q_{kax}} \approx {{e\; 2\left( {{Qka}\left( {x + {\Delta \; x}} \right)} \right)} - {e\; 2\left( {{Qka}\left( {x - {\Delta \; x}} \right)} \right)}}} & (12) \\{\frac{{\partial e}\; 2}{\partial Q_{kay}} \approx {{e\; 2\left( {{Qka}\left( {y + {\Delta \; y}} \right)} \right)} - {e\; 2\left( {{Qka}\left( {y - {\Delta \; y}} \right)} \right)}}} & (13) \\{{\Delta \; {Qka}} = {\alpha \times \left( {{- \frac{{\partial e}\; 2}{\partial Q_{kax}}},{- \frac{{\partial e}\; 2}{\partial Q_{kay}}}} \right)}} & (14)\end{matrix}$

In this physics inspired optimization, ΔQ_(ka) is regarded as a forceimposed on the endpoint Q_(ka) of an spring-like edge E_(ik). As for thesecond concept of force composition, we defineE[P_(ij)]={(Q_(oa)=P_(ij),Q_(ob),c),o∈[1,#(E[P_(ij)])]} which is asubset of E_(i). And the force imposed on P_(ij) when consideringdifferent edges can be denoted as ΔQ_(oa). Thus ΔP_(ij) can beapproximated with:

ΔPij=Σ _(o=1) ^(#(E[Pij])) ΔQoa  (15)

Algorithm 2 Physics Inspired Optimization Initialize: average P_(i)while e decreases do  for all j do   get the subset E[P_(ij)]   for allo do   calculate the force imposed on Q_(oa) = P_(ij) according toEquation 12 13 14   end for   calculate ΔP_(ij) by Equation 15, andupdate P_(ij)  end for  calculate e at updated P_(i) end while

As mentioned before, Equation 15 naturally obeys the parallelogram lawof force composition. In case of potential confusion, both ΔQ_(oa) andΔQ_(ka) are calculated according to Equation 14 (k is the index in E_(i)and o is the index in E_(i)'s subset E[P_(ij)]). And to summarize, PIO'sefficiency primarily comes from Equation 10's O(1) complexity whileEquation 2's is at least O(N) (NOB).

FIG. 18D: Left: qualitative results on LSUN validation set. Thevisualized feature map merges wf, ww, and wc by a pixel-wise maxoperation, yet they are used independently in PIO. Right: typicalfailure cases in which a wrong topology produces the lowest energy.

TABLE 1 Quantitative results on LSUN test set. Method ep (%) ec (%)Hedau et al. (2009) [11] 24.23 15.48 Mallya et al. (2015) [17] 16.7111.02 Dasgupta et al. (2016) [3] 10.63 8.20 Ren et al. (2016) [27] 7.575.23 Ours 5.29 3.84

Experiments LSUN Results

LSUN is a room layout estimation dataset consisted of 4000 training, 394validation, and 1000 held-out testing samples. Two standard metrics areused for evaluation: (1) e_(conner). Corner (conjunction) error is theEuclidean distance between estimated coordinates of P_(i) and groundtruth. Because of resolution diversity, e_(conner) is normalized byimage diagonal length. (2) e_(pixel). By converting P_(i) into maskrepresentation like the ground truth in FIG. 18D, pixel error measuresthe ratio of mislabeled pixels to all pixels. (For e_(pixel)'s labelambiguity problem, LSUN official evaluation codes automatically maximizethe overlap.) For a large-scale evaluation, both metrics are averagedover images. On validation set, official evaluation codes provided byLSUN committee are used. Third-party evaluation results on test set arereported in Table 1. The proposed method outperforms conventional method[11] and FCNbased methods [17][3][27] on both metrics. Qualitativeresults and failure cases on validation set are demonstrated by FIG.18D.

FIG. 18D shows examples of qualitative results on an LSUN validation seton the left as visualized feature map merges of wf, ww, and wc by apixel-wise max operation according to exemplary embodiments. The rightside shows typical failure cases in which a wrong topology produces thelowest energy according to exemplary embodiments. Referring to FIG. 18D,part (a) shows a typical easy case, in which most edge pixels arevisible and the feature map captures their locations accurately. For oneembodiment, the visualized edge map gets twisted temporarily near 30thiteration because of force composition and PIO finally aligns it withthe true layout.

Parts (b)-(d) in FIG. 18D show some cases in which the feature maps failto locate edges accurately where the black circles are, leading torelatively higher e_(conner). Reasons are many-folded, such as severeocclusion (b), insufficient feature map resolution (c), and misleadingtexture (d).

TABLE 2 Quantitative results on Hedau test set. Method ep (%) Hedau etal. (2009) [11] 21.20 Del Pero et al. (2013) [5] 12.70 Mallya et al.(2015) [17] 12.83 Dasgupta et al. (2016) [3] 9.73 Ren et al. (2016) [27]8.67 Ours 6.60

TABLE 3 Average running time per frame (artpf) comparison. NOB PIO artpf(s) 35.41 1.79 ep (%) 5.42 5.48 ec (%) 3.88 3.95

Referring to part (e) in FIG. 18D, the room is no longer a strict box ifwe consider the cabinet as a part of wall. Actually, those separatedwall-ceiling edges are successfully captured by the feature map andaligned by PIO. However, the annotation protocol takes the cabinet asocclusion. Part (f) of FIG. 18D shows a heavily-occluded case. For oneembodiment, semantic transfer allows the network to extrapolate theexistence of wall-floor edges behind the bed, but the conjunction in theblack circle is not accurately localized.

Although not 100% accurate, parts (a)-(f) in FIG. 18D are regarded assuccessful cases as the output topologies are right. Parts (g)-(h) inFIG. 18D are two typical failure cases in which a wrong topologyproduces the lowest energy. Failure in Part (g) of FIG. 18D is caused byover-fitting. The network extrapolates there are wall-floor edges behindthe bed, yet the annotation protocol does not. Part (h) of FIG. 18Ddemonstrates another type of failures caused by structure ambiguity.Again, this scene is no longer a strict box as some parts of the wallprotrude outwards. The network recognizes them as ceiling but theannotation protocol does not, causing PIO to output a wrong topology.

Hedau Results

The Hedau dataset is presented by Reference [11] being consisted of 209training samples and 105 testing samples. On Hedau test set, for oneembodiment, the model trained with LSUN training set can be directlyevaluated. FIG. 18E shows examples of qualitative results on a Hedauvalidation set on the left as visualized feature map merges of wf, ww,and wc by a pixel-wise max operation according to exemplary embodiments.As FIG. 18E shows, this model extracts reliable features acrossdatasets. Consistent to the literature, pixel error can be aquantitative metric. The disclosed techniques can report better resultsthan conventional methods like those described in References [5] and[11] and FCN-based methods like References [3], [17] and [27] (Table 2).Overall pixel error (6.60%) on Hedau test set is higher than that(5.29%) on LSUN test set because the ground truth mask annotated byHedau dataset is more strict (typically shown by part (j) in FIG. 18E).

Hyper Parameters and Efficiency

In Algorithm 2, whether e decreases is determined by a threshold of10-6. This threshold is related to the numerical scalability of e.During implementation, for one embodiment, e=−CO instead of e=exp(−CO)because of equivalence, and e's numerical scalability is around −15which has already been shown in videos mentioned above. (2) Scalingfactor α is self-adaptive to ensure that the gradients' (forces') lengthis between 1 and 3. This restricts the conjunction to move only a littlein one iteration, as the videos show. (3) The influence of window sizeΔx (=Δy) is evaluated on LSUN validation set, and the quantitativeresults are demonstrated by FIG. 18F. FIG. 18F is a graph of errorversus window size on the LSUN validation set according to exemplaryembodiments. As the window size grows from 1 pixel to 10 pixels, bothmetrics show a trend of increase. Since PIO can be regarded as analignment algorithm, this is not surprising because calculatinggradients in a larger window size leads to a weaker ability toaccurately capture local structure.

For one embodiment, average running time per frame on LSUN validationset can be evaluated with NOB and PIO. NOA is not evaluated because ofintractability. The results are provided in Table 3, showing that PIObrings dramatic speedup against NOB without causing noticeable accuracyloss. Evaluation can be implemented with MATLAB and the disclosedtechniques can be used with real-time applications.

FIG. 18G is a diagram of a comparison of two techniques forphysics-inspired optimization according to exemplary embodiments.Specifically, FIG. 18G shows a comparison of the naïve andphysics-inspired optimization. The operations of the optimizations arepresented as steps and the squares refer to operations in the fiveboxes. The first two boxes ST Features and Conjunction Point Coordinatesare inputs to both processes. The third box, energy objective isperformed for both steps 1. The next operation labelled step 3 is wherethe two optimizations differ in that the gradient operations aredifferent as described above. The remaining operations are updates andrepeats until the optimizations are complete.

Summary

Disclosed examples and embodiments implement alternative methods forroom layout estimation. With a very deep semantic transfer FCN, reliableedge features can be extracted under various circumstances. A newinference scheme, which is inspired by mechanics concepts, is alsoprovided. The effectiveness of the disclosed methods can be illustratedby extensive quantitative experiments on public datasets.

REFERENCES

References [1] through [29] referred to in this detailed description areidentified as follows.

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Graphics System Overview

FIG. 19 is a block diagram of a processing system 1900 according to anexemplary embodiment. In various embodiments, the system 1900 includesone or more processors 1902 and one or more graphics processors 1908,and may be a single processor desktop system, a multiprocessorworkstation system, or a server system having a large number ofprocessors 1902 or processor cores 107. In one embodiment, the system1900 is a processing platform incorporated within a system-on-a-chip(SoC) integrated circuit for use in mobile, handheld, or embeddeddevices.

An embodiment of system 1900 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 1900 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 1900 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 1900 is a television or set topbox device having one or more processors 1902 and a graphical interfacegenerated by one or more graphics processors 1908.

In some embodiments, the one or more processors 1902 each include one ormore processor cores 1907 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 1907 is configured to process aspecific instruction set 1909. In some embodiments, instruction set 1909may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 1907 may each processa different instruction set 1909, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 1907may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 1902 includes cache memory 1904.Depending on the architecture, the processor 1902 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 1902. In some embodiments, the processor 1902 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 1907 using knowncache coherency techniques. A register file 1906 is additionallyincluded in processor 1902 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 1902.

In some embodiments, processor 1902 is coupled with a processor bus 1910to transmit communication signals such as address, data, or controlsignals between processor 1902 and other components in system 1900. Inone embodiment, the system 100 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 1916 and an Input Output(I/O) controller hub 1930. A memory controller hub 1916 facilitatescommunication between a memory device and other components of system1900, while an I/O Controller Hub (ICH) 1930 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 1916 is integrated within the processor.

Memory device 1920 can be a dynamic random access memory (DRAM) device,a static random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment, the memorydevice 1920 can operate as system memory for the system 1900, to storedata 1922 and instructions 1921 for use when the one or more processors1902 executes an application or process. Memory controller hub 1916 alsocouples with an optional external graphics processor 1912, which maycommunicate with the one or more graphics processors 1908 in processors1902 to perform graphics and media operations.

In some embodiments, ICH 1930 enables peripherals to connect to memorydevice 1920 and processor 1902 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 1946, afirmware interface 1928, a wireless transceiver 1926 (e.g., Wi-Fi,Bluetooth), a data storage device 1924 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 1940 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 1942 connect input devices, suchas keyboard and mouse 1944 combinations. A network controller 1934 mayalso couple with ICH 1930. In some embodiments, a high-performancenetwork controller (not shown) couples with processor bus 1910. It willbe appreciated that the system 1900 shown is exemplary and not limiting,as other types of data processing systems that are differentlyconfigured may also be used. For example, the I/O controller hub 1930may be integrated within the one or more processor 1902, or the memorycontroller hub 1916 and I/O controller hub 1930 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 1912.

FIG. 20 is a block diagram of an exemplary embodiment of a processor2000 having one or more processor cores 2002A-2002N, an integratedmemory controller 2014, and an integrated graphics processor 2008. Thoseelements of FIG. 20 having the same reference numbers (or names) as theelements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such. Processor 2000 can include additional cores up to and includingadditional core 2002N represented by the dashed lined boxes. Each ofprocessor cores 2002A-2002N includes one or more internal cache units2004A-2004N. In some embodiments, each processor core also has access toone or more shared cached units 2006.

The internal cache units 2004A-2004N and shared cache units 2006represent a cache memory hierarchy within the processor 2000. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of sharedmid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 2006 and2004A-2004N.

In some embodiments, processor 2000 may also include a set of one ormore bus controller units 216 and a system agent core 2010. The one ormore bus controller units 216 manage a set of peripheral buses, such asone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 2010 provides management functionality forthe various processor components. In some embodiments, system agent core2010 includes one or more integrated memory controllers 2014 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 2002A-2002Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 210 includes components for coordinating andoperating cores 2002A-2002N during multi-threaded processing. Systemagent core 210 may additionally include a power control unit (PCU),which includes logic and components to regulate the power state ofprocessor cores 2002A-2002N and graphics processor 2008.

In some embodiments, processor 2000 additionally includes graphicsprocessor 2008 to execute graphics processing operations. In someembodiments, the graphics processor 2008 couples with the set of sharedcache units 2006, and the system agent core 2010, including the one ormore integrated memory controllers 2014. In some embodiments, a displaycontroller 2011 is coupled with the graphics processor 2008 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 2011 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 2008 or system agent core 2010.

In some embodiments, a ring based interconnect unit 2012 is used tocouple the internal components of the processor 2000. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 208 couples with the ring interconnect 2012 via an I/O link2013.

The exemplary I/O link 2013 represents at least one of multiplevarieties of I/O interconnects, including an on-package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 218, such as an eDRAM module.In some embodiments, each of the processor cores 202A-202N and graphicsprocessor 208 use embedded memory modules 218 as a shared Last LevelCache.

In some embodiments, processor cores 2002A-2002N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 2002A-2002N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 2002A-2002Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 2002A-2002N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor200 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 21 is a block diagram of a graphics processor 2100, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 300 includes amemory interface 2114 to access memory. Memory interface 314 can be aninterface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 2100 also includes a displaycontroller 2102 to drive display output data to a display device 2120.Display controller 2102 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 2100includes a video codec engine 306 to encode, decode, or transcode mediato, from, or between one or more media encoding formats, including, butnot limited to Moving Picture Experts Group (MPEG) formats such asMPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, aswell as the Society of Motion Picture & Television Engineers (SMPTE)421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such asJPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 2100 includes a block imagetransfer (BLIT) engine 2104 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 310. In someembodiments, GPE 2110 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 2110 includes a 3D pipeline 2112 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 2112 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 315.While 3D pipeline 2112 can be used to perform media operations, anembodiment of GPE 310 also includes a media pipeline 2116 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 2116 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 2106. In some embodiments, media pipeline 2116 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 2115. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 2115.

In some embodiments, 3D/Media subsystem 2115 includes logic forexecuting threads spawned by 3D pipeline 2112 and media pipeline 2116.In one embodiment, the pipelines send thread execution requests to3D/Media subsystem 2115, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 2115 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

Graphics Processing Engine

FIG. 22 is a block diagram of a graphics processing engine 2210 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 2210 is a version ofthe GPE 2210 shown in FIG. 21. Elements of FIG. 22 having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. For example, the 3Dpipeline 2212 and media pipeline 2216 of FIG. 3 are illustrated. Themedia pipeline 2216 is optional in some embodiments of the GPE 2210 andmay not be explicitly included within the GPE 410. For example, and inat least one embodiment, a separate media and/or image processor iscoupled to the GPE 2210.

In some embodiments, GPE 2210 couples with or includes a commandstreamer 2203, which provides a command stream to the 3D pipeline 2112and/or media pipelines 2116. In some embodiments, command streamer 2203is coupled with memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In some embodiments,command streamer 2203 receives commands from the memory and sends thecommands to 3D pipeline 2112 and/or media pipeline 2116. The commandsare directives fetched from a ring buffer, which stores commands for the3D pipeline 2112 and media pipeline 2116. In one embodiment, the ringbuffer can additionally include batch command buffers storing batches ofmultiple commands. The commands for the 3D pipeline 2112 can alsoinclude references to data stored in memory, such as but not limited tovertex and geometry data for the 3D pipeline 2112 and/or image data andmemory objects for the media pipeline 2116. The 3D pipeline 2112 andmedia pipeline 2116 process the commands and data by performingoperations via logic within the respective pipelines or by dispatchingone or more execution threads to a graphics core array 2214.

In various embodiments, the 3D pipeline 2112 can execute one or moreshader programs, such as vertex shaders, geometry shaders, pixelshaders, fragment shaders, compute shaders, or other shader programs, byprocessing the instructions and dispatching execution threads to thegraphics core array 2214. The graphics core array 2214 provides aunified block of execution resources. Multi-purpose execution logic(e.g., execution units) within the graphic core array 2214 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 2214 also includesexecution logic to perform media functions, such as video and/or imageprocessing. In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallel generalpurpose computational operations, in addition to graphics processingoperations. The general-purpose logic can perform processing operationsin parallel or in conjunction with general purpose logic within theprocessor core(s) 1907 of FIG. 19 or core 2002A-2002N as in FIG. 20.

Output data generated by threads executing on the graphics core array2214 can output data to memory in a unified return buffer (URB) 2218.The URB 2218 can store data for multiple threads. In some embodiments,the URB 2218 may be used to send data between different threadsexecuting on the graphics core array 2214. In some embodiments, the URB2218 may additionally be used for synchronization between threads on thegraphics core array and fixed function logic within the shared functionlogic 2220.

In some embodiments, graphics core array 2214 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 2210. In one embodiment, the executionresources are dynamically scalable, such that execution resources may beenabled or disabled as needed.

The graphics core array 2214 couples with shared function logic 2220that includes multiple resources that are shared between the graphicscores in the graphics core array. The shared functions within the sharedfunction logic 2220 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 2214. In variousembodiments, shared function logic 2220 includes but is not limited tosampler 2221, math 2222, and inter-thread communication (ITC) 2223logic. Additionally, some embodiments implement one or more cache(s)2225 within the shared function logic 2220. A shared function isimplemented where the demand for a given specialized function isinsufficient for inclusion within the graphics core array 2214. Insteada single instantiation of that specialized function is implemented as astand-alone entity in the shared function logic 2220 and shared amongthe execution resources within the graphics core array 2214. The preciseset of functions that are shared between the graphics core array 2214and included within the graphics core array 2214 varies betweenembodiments.

FIG. 23 is a block diagram of another exemplary embodiment of a graphicsprocessor 500. Elements of FIG. 23 having the same reference numbers (ornames) as the elements of any other figure herein can operate orfunction in any manner similar to that described elsewhere herein, butare not limited to such.

In some embodiments, graphics processor 2300 includes a ringinterconnect 2302, a pipeline front-end 2304, a media engine 2337, andgraphics cores 2380A-2380N. In some embodiments, ring interconnect 2302couples the graphics processor to other processing units, includingother graphics processors or one or more general-purpose processorcores. In some embodiments, the graphics processor is one of manyprocessors integrated within a multi-core processing system.

In some embodiments, graphics processor 2300 receives batches ofcommands via ring interconnect 2302. The incoming commands areinterpreted by a command streamer 2303 in the pipeline front-end 2304.In some embodiments, graphics processor 2300 includes scalable executionlogic to perform 3D geometry processing and media processing via thegraphics core(s) 2380A-2380N. For 3D geometry processing commands,command streamer 2303 supplies commands to geometry pipeline 2336. Forat least some media processing commands, command streamer 2303 suppliesthe commands to a video front end 2334, which couples with a mediaengine 2337. In some embodiments, media engine 2337 includes a VideoQuality Engine (VQE) 2330 for video and image post-processing and amulti-format encode/decode (MFX) 2333 engine to providehardware-accelerated media data encode and decode. In some embodiments,geometry pipeline 2336 and media engine 2337 each generate executionthreads for the thread execution resources provided by at least onegraphics core 2380A.

In some embodiments, graphics processor 2300 includes scalable threadexecution resources featuring modular cores 2380A-2380N (sometimesreferred to as core slices), each having multiple sub-cores 2350A-2350N,2360A-2360N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 2300 can have any number of graphicscores 2380A through 2380N. In some embodiments, graphics processor 2300includes a graphics core 2380A having at least a first sub-core 2350Aand a second sub-core 2360A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 2350A).In some embodiments, graphics processor 2300 includes multiple graphicscores 2380A-2380N, each including a set of first sub-cores 2350A-2350Nand a set of second sub-cores 2360A-2360N. Each sub-core in the set offirst sub-cores 2350A-2350N includes at least a first set of executionunits 2352A-2352N and media/texture samplers 2354A-2354N. Each sub-corein the set of second sub-cores 2360A-2360N includes at least a secondset of execution units 2362A-562N and samplers 2364A-2364N. In someembodiments, each sub-core 2350A-2350N, 2360A-2360N shares a set ofshared resources 2370A-2370N. In some embodiments, the shared resourcesinclude shared cache memory and pixel operation logic. Other sharedresources may also be included in the various embodiments of thegraphics processor.

Execution Units

FIG. 24 illustrates thread execution logic 2400 including an array ofprocessing elements employed in some exemplary embodiments of a GPE.Elements of FIG. 24 having the same reference numbers (or names) as theelements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, thread execution logic 2400 includes a shaderprocessor 2402, a thread dispatcher 2404, instruction cache 2406, ascalable execution unit array including a plurality of execution units2408A-2408N, a sampler 2410, a data cache 2412, and a data port 2414. Inone embodiment, the scalable execution unit array can dynamically scaleby enabling or disabling one or more execution units (e.g., any ofexecution unit 2408A, 2408B, 2408C, 2408D, through 2408N-1 and 2408N)based on the computational requirements of a workload. In oneembodiment, the included components are interconnected via aninterconnect fabric that links to each of the components. In someembodiments, thread execution logic 2400 includes one or moreconnections to memory, such as system memory or cache memory, throughone or more of instruction cache 2406, data port 2414, sampler 2410, andexecution units 2408A-2408N. In some embodiments, each execution unit(e.g. 2408A) is a stand-alone programmable general purpose computationalunit that is capable of executing multiple simultaneous hardware threadswhile processing multiple data elements in parallel for each thread. Invarious embodiments, the array of execution units 2408A-2408N isscalable to include any number individual execution units.

In some embodiments, the execution units 2408A-2408N are primarily usedto execute shader programs. A shader processor 2402 can process thevarious shader programs and dispatch execution threads associated withthe shader programs via a thread dispatcher 2404. In one embodiment, thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units2408A-2408N. For example, the geometry pipeline (e.g., 2336 of FIG. 23)can dispatch vertex, tessellation, or geometry shaders to the threadexecution logic 2400 (FIG. 24) for processing. In some embodiments,thread dispatcher 604 can also process runtime thread spawning requestsfrom the executing shader programs.

In some embodiments, the execution units 2408A-2408N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 2408A-2408N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units2408A-2408N causes a waiting thread to sleep until the requested datahas been returned. While the waiting thread is sleeping, hardwareresources may be devoted to processing other threads. For example,during a delay associated with a vertex shader operation, an executionunit can perform operations for a pixel shader, fragment shader, oranother type of shader program, including a different vertex shader.

Each execution unit in execution units 2408A-2408N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 608A-608N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

One or more internal instruction caches (e.g., 2406) are included in thethread execution logic 2400 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,2412) are included to cache thread data during thread execution. In someembodiments, a sampler 2410 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 2410 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 2400 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor2402 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 2402 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 2402dispatches threads to an execution unit (e.g., 2408A) via threaddispatcher 2404. In some embodiments, pixel shader 2402 uses texturesampling logic in the sampler 2410 to access texture data in texturemaps stored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 2414 provides a memory accessmechanism for the thread execution logic 2400 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 614 includes or couples to one or more cachememories (e.g., data cache 2412) to cache data for memory access via thedata port.

FIG. 25 is a block diagram illustrating a graphics processor instructionformats 2500 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 2500 described and illustrated aremacro-instructions, in that they are instructions supplied to theexecution unit, as opposed to micro-operations resulting frominstruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 2510. A 64-bitcompacted instruction format 2530 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 2510 provides access toall instruction options, while some options and operations arerestricted in the 64-bit instruction format 2530. The nativeinstructions available in the 64-bit instruction format 2530 vary byembodiment. In some embodiments, the instruction is compacted in partusing a set of index values in an index field 2513. The execution unithardware references a set of compaction tables based on the index valuesand uses the compaction table outputs to reconstruct a nativeinstruction in the 128-bit instruction format 2510.

For each format, instruction opcode 2512 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 2514 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 2510 an exec-size field2516 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 2516 is not available foruse in the 64-bit compact instruction format 2530.

Some execution unit instructions have up to three operands including twosource operands, src0 2520, src1 2522, and one destination 2518. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 2524), where the instructionopcode 2512 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 2510 includes anaccess/address mode field 2526 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 2510 includes anaccess/address mode field 2526, which specifies an address mode and/oran access mode for the instruction. In one embodiment, the access modeis used to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 2512bit-fields to simplify Opcode decode 2540. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 2542 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 2542 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 2544 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 2546 includesa mix of instructions, including synchronization instructions (e.g.,wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel mathinstruction group 2548 includes component-wise arithmetic instructions(e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). Theparallel math group 2548 performs the arithmetic operations in parallelacross data channels. The vector math group 750 includes arithmeticinstructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). Thevector math group performs arithmetic such as dot product calculationson vector operands.

Graphics Pipeline

FIG. 26 is a block diagram of another embodiment of a graphics processor800. Elements of FIG. 26 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 2600 includes a graphicspipeline 2620, a media pipeline 2630, a display engine 2640, threadexecution logic 2650, and a render output pipeline 2670. In someembodiments, graphics processor 2600 is a graphics processor within amulti-core processing system that includes one or more general purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 2600 via a ring interconnect 2602. In someembodiments, ring interconnect 802 couples graphics processor 2600 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 802 areinterpreted by a command streamer 2603, which supplies instructions toindividual components of graphics pipeline 2620 or media pipeline 2630.

In some embodiments, command streamer 2603 directs the operation of avertex fetcher 2605 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 2603. In someembodiments, vertex fetcher 805 provides vertex data to a vertex shader2607, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 805 andvertex shader 2607 execute vertex-processing instructions by dispatchingexecution threads to execution units 2652A-2652B via a thread dispatcher2631.

In some embodiments, execution units 2652A-2652B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 2652A-2652B have anattached L1 cache 2651 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 2620 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 2611 configures thetessellation operations. A programmable domain shader 2617 providesback-end evaluation of tessellation output. A tessellator 2613 operatesat the direction of hull shader 2611 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 2620. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 2611, tessellator 2613, and domain shader 2617) canbe bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 2619 via one or more threads dispatched to executionunits 2652A-2652B, or can proceed directly to the clipper 2629. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader2619 receives input from the vertex shader 2607. In some embodiments,geometry shader 2619 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 2629 processes vertex data. The clipper2629 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 2673 in the render output pipeline2670 dispatches pixel shaders to convert the geometric objects intotheir per pixel representations. In some embodiments, pixel shader logicis included in thread execution logic 2650. In some embodiments, anapplication can bypass the rasterizer and depth test component 2673 andaccess un-rasterized vertex data via a stream out unit 2623.

The graphics processor 2600 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 2652A-2652B and associated cache(s) 2651,texture and media sampler 2654, and texture/sampler cache 2658interconnect via a data port 2656 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 2654, caches 2651, 2658 and execution units2652A-2652B each have separate memory access paths.

In some embodiments, render output pipeline 2670 contains a rasterizerand depth test component 2673 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache2678 and depth cache 2679 are also available in some embodiments. Apixel operations component 2677 performs pixel-based operations on thedata, though in some instances, pixel operations associated with 2Doperations (e.g. bit block image transfers with blending) are performedby the 2D engine 2641, or substituted at display time by the displaycontroller 2643 using overlay display planes. In some embodiments, ashared L3 cache 2675 is available to all graphics components, allowingthe sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 2630 includes amedia engine 2637 and a video front end 2634. In some embodiments, videofront end 2634 receives pipeline commands from the command streamer2603. In some embodiments, media pipeline 2630 includes a separatecommand streamer. In some embodiments, video front-end 2634 processesmedia commands before sending the command to the media engine 2637. Insome embodiments, media engine 2637 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic2650 via thread dispatcher 2631.

In some embodiments, graphics processor 2600 includes a display engine840. In some embodiments, display engine 2640 is external to processor2600 and couples with the graphics processor via the ring interconnect2602, or some other interconnect bus or fabric. In some embodiments,display engine 2640 includes a 2D engine 2641 and a display controller2643. In some embodiments, display engine 2640 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 2643 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 2620 and media pipeline 2630 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 27A is a block diagram illustrating a graphics processor commandformat 2700 according to some embodiments. FIG. 27B is a block diagramillustrating a graphics processor command sequence 2710 according to anembodiment. The solid lined boxes in FIG. 27A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 2700 of FIG. 27A includes data fields to identify atarget client 2702 of the command, a command operation code (opcode)2704, and the relevant data 2706 for the command. A sub-opcode 2705 anda command size 2708 are also included in some commands.

In some embodiments, client 2702 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 2704 and, if present, sub-opcode 2705 to determine theoperation to perform. The client unit performs the command usinginformation in data field 2706. For some commands an explicit commandsize 908 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 27B shows an exemplary graphics processorcommand sequence 2710. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 maybegin with a pipeline flush command 2712 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 2722 and the media pipeline 2724 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 2712 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 2713 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 2713is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command 2712 isrequired immediately before a pipeline switch via the pipeline selectcommand 2713.

In some embodiments, a pipeline control command 2714 configures agraphics pipeline for operation and is used to program the 3D pipeline2722 and the media pipeline 2724. In some embodiments, pipeline controlcommand 2714 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 2714 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, commands for the return buffer state 2716 are usedto configure a set of return buffers for the respective pipelines towrite data. Some pipeline operations require the allocation, selection,or configuration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments,configuring the return buffer state 2716 includes selecting the size andnumber of return buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 2720,the command sequence is tailored to the 3D pipeline 2722 beginning withthe 3D pipeline state 2730 or the media pipeline 2724 beginning at themedia pipeline state 2740.

The commands to configure the 3D pipeline state 930 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 2730 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 2732 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 2732 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 2732command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 2732 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 2722 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 2722 is triggered via an execute 2734command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 2710follows the media pipeline 2724 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 2724 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed and media decode can be performed in wholeor in part using resources provided by one or more general purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIMD vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 2724 is configured in a similarmanner as the 3D pipeline 2722. A set of commands to configure the mediapipeline state 2740 are dispatched or placed into a command queue beforethe media object commands 2742. In some embodiments, commands for themedia pipeline state 2740 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 2740 also support the use of oneor more pointers to “indirect” state elements that contain a batch ofstate settings.

In some embodiments, media object commands 2742 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 2742. Once the pipeline state is configured andmedia object commands 2742 are queued, the media pipeline 924 istriggered via an execute command 2744 or an equivalent execute event(e.g., register write). Output from media pipeline 2724 may then be postprocessed by operations provided by the 3D pipeline 2722 or the mediapipeline 2724. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 28 illustrates exemplary graphics software architecture for a dataprocessing system 2800 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application2810, an operating system 2820, and at least one processor 2830. In someembodiments, processor 2830 includes a graphics processor 2832 and oneor more general-purpose processor core(s) 2834. The graphics application2810 and operating system 2820 each execute in the system memory 1050 ofthe data processing system.

In some embodiments, 3D graphics application 2810 contains one or moreshader programs including shader instructions 2812. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 2814 in a machinelanguage suitable for execution by the general-purpose processor core2834. The application also includes graphics objects 1016 defined byvertex data.

In some embodiments, operating system 2820 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 1020 can support agraphics API 2822 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 2820uses a front-end shader compiler 2824 to compile any shader instructions2812 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 2810. In some embodiments, the shader instructions 2812 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 2826 contains a back-endshader compiler 2827 to convert the shader instructions 2812 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 2812 in the GLSL high-level language are passed to a usermode graphics driver 2826 for compilation. In some embodiments, usermode graphics driver 2826 uses operating system kernel mode functions2828 to communicate with a kernel mode graphics driver 2829. In someembodiments, kernel mode graphics driver 1029 communicates with graphicsprocessor 2832 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 29 is a block diagram illustrating an IP core development system1100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system1100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility2930 can generate a software simulation 2910 of an IP core design in ahigh level programming language (e.g., C/C++). The software simulation2910 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 2912. The simulation model 2912 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 2915 can then be created or synthesized from thesimulation model 2912. The RTL design 2915 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 2915, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 2915 or equivalent may be further synthesized by thedesign facility into a hardware model 2920, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 2965 using non-volatile memory 2940 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 2950 or wireless connection 2960. Thefabrication facility 2965 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

Exemplary System on a Chip Integrated Circuit

FIGS. 30-32 illustrate exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general purpose processor cores.

FIG. 30 is a block diagram illustrating an exemplary system on a chipintegrated circuit 3000 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 1200includes one or more application processor(s) 3005 (e.g., CPUs), atleast one graphics processor 3010, and may additionally include an imageprocessor 3015 and/or a video processor 3020, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 3000 includes peripheral or bus logic including a USBcontroller 1225, UART controller 3030, an SPI/SDIO controller 3035, andan I²S/I²C controller 3040. Additionally, the integrated circuit caninclude a display device 3045 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 1250 and a mobileindustry processor interface (MIPI) display interface 3055. Storage maybe provided by a flash memory subsystem 3060 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 1265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine3070.

FIG. 31 is a block diagram illustrating an exemplary graphics processor3110 of a system on a chip integrated circuit that may be fabricatedusing one or more IP cores, according to an embodiment. Graphicsprocessor 3110 can be a variant of the graphics processor 3010 of FIG.30. Graphics processor 3110 includes a vertex processor 3105 and one ormore fragment processor(s) 3115A-3115N (e.g., 3115A, 3115B, 3115C,3115D, through 3115N−1, and 3115N). Graphics processor 3110 can executedifferent shader programs via separate logic, such that the vertexprocessor 3105 is optimized to execute operations for vertex shaderprograms, while the one or more fragment processor(s) 3115A-3115Nexecute fragment (e.g., pixel) shading operations for fragment or pixelshader programs. The vertex processor 3105 performs the vertexprocessing stage of the 3D graphics pipeline and generates primitivesand vertex data. The fragment processor(s) 3115A-3115N use the primitiveand vertex data generated by the vertex processor 3105 to produce aframebuffer that is displayed on a display device. In one embodiment,the fragment processor(s) 3115A-3115N are optimized to execute fragmentshader programs as provided for in the OpenGL API, which may be used toperform similar operations as a pixel shader program as provided for inthe Direct 3D API.

Graphics processor 3110 additionally includes one or more memorymanagement units (MMUs) 3120A-3120B, cache(s) 3125A-3125B, and circuitinterconnect(s) 3130A-3130B. The one or more MMU(s) 3120A-3120B providefor virtual to physical address mapping for graphics processor 3110,including for the vertex processor 1305 and/or fragment processor(s)3115A-3115N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 3125A-3125B. In one embodiment, the one or more MMU(s)3120A-3120B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 3005, image processor 3015, and/or video processor 3020 ofFIG. 30, such that each processor 3005-3020 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 3130A-3130B enable graphics processor 3110 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

FIG. 32 is a block diagram illustrating an additional exemplary graphicsprocessor 3210 of a system on a chip integrated circuit that may befabricated using one or more IP cores, according to an embodiment.Graphics processor 3210 can be a variant of the graphics processor 3010of FIG. 30. Graphics processor 3210 includes the one or more MMU(s)3120A-3120B, cache(s) 3125A-3125B, and circuit interconnect(s)3130A-1330B of the integrated circuit 3100 of FIG. 31.

Graphics processor 3210 includes one or more shader core(s) 3215A-3215N(e.g., 3215A, 3215B, 3215C, 3215D, 3215E, 3215F, through 3215N−1, and3115N), which provides for a unified shader core architecture in which asingle core or type or core can execute all types of programmable shadercode, including shader program code to implement vertex shaders,fragment shaders, and/or compute shaders. The exact number of shadercores present can vary among embodiments and implementations.Additionally, graphics processor 3210 includes an inter-core taskmanager 3205, which acts as a thread dispatcher to dispatch executionthreads to one or more shader core(s) 3215A-3215N and a tiling unit 3218to accelerate tiling operations for tile-based rendering, in whichrendering operations for a scene are subdivided in image space, forexample to exploit local spatial coherence within a scene or to optimizeuse of internal caches.

Examples and embodiments of the present invention include methods andapparatus for discriminative semantic transfer and physics-inspiredoptimization in deep learning.

According to one example, a pipeline framework to run deep learningtraining comprises a first stage, second stage, and a third stage. Thefirst stage is configured to receive a sequence of training images in aconvolutional neural network (CNN) to describe objects of a clutteredscene as a semantic segmentation mask. The second stage is configured toreceive the semantic segmentation mask in a semantic segmentationnetwork and to produce semantic features. The third stage is configuredto use weights from the first stage as feature extractors and weightsfrom the second stage as classifiers in order to identify edges of thecluttered scene using the semantic features.

In one example, the third stage is further configured to fine-tune theCNN that receives the training images and identifies the edges of thecluttered scene.

In one example, the second stage is further configured to use a softmaxoperation in the semantic segmentation network.

In one example, objects of the cluttered scene are part of a room layoutdataset.

In one example, the room layout dataset comprises pixels and wherein thesecond stage is configured to treat each pixel of the room layoutdataset as a sample in a fully connected layer of the semanticsegmentation network.

In one example, the second stage is further configured to modelrelationships between the room layout dataset and objects in the CNN.

In one example, the third stage is further configured to label edges ofthe cluttered scene.

According to one example, a data processing system comprises aprocessing core, an I/O hub controller, and a graphics processor. Theprocessing core is configured with a convolutional neural network (CNN).The I/O hub controller is coupled to the processing core and configuredto provide network, data storage, and access to the processing core. Thegraphics processor is coupled to the I/O hub controller and configuredto implement a first stage, second stage and third stage. The firststage is configured to receive a sequence of training images in aconvolutional neural network (CNN) to describe objects of a clutteredscene as a semantic segmentation mask. The second stage configured toreceive the semantic segmentation mask in a semantic segmentationnetwork and to produce semantic features. The third stage is configuredto use weights from the first stage as feature extractors and weightsfrom the second stage as classifiers in order to identify edges of thecluttered scene using the semantic features.

In on example, the third stage is further configured to fine-tune theCNN that receives the training images and identifies the edges of thecluttered scene.

In one example, the second stage is further configured to use a softmaxoperation in the semantic segmentation network.

In one example, objects of the cluttered scene are part of a room layoutdataset comprising pixels.

In one example, the second stage is further configured to treat eachpixel of the room layout dataset as a sample in a fully connected layerof the semantic segmentation network.

In one example, the second stage is further configured to modelrelationships between the room layout dataset and objects in the CNN.

In one example, the third stage is further configured to label edges ofthe cluttered scene.

According to one example, a computation training method for aconvolutional neural network (CNN) includes receiving a sequence oftraining images in the CNN of a first stage to describe objects of acluttered scene as a semantic segmentation mask; receiving the semanticsegmentation mask in a semantic segmentation network of a second stageto produce semantic features; and using weights from the first stage asfeature extractors and weights from the second stage as classifiers in athird stage to identify edges of the cluttered scene using the semanticfeatures.

In one example, the CNN is fine-tuned in the third stage.

In one example, using a softmax operation in the semantic segmentationnetwork of the second stage.

In one example, objects of the cluttered scene are part of a room layoutdataset comprising pixels.

In one example, each pixel in the room layout dataset is treated as asample in a fully connected layer of the semantic segmentation networkof the second stage.

In one example, relationships in the second stage are modeled betweenthe room layout dataset and objects in the CNN.

In one example, edges of the cluttered scene are labeled pixel-wise inthe third stage.

According to one example, a method of training a deep learning-basedclassification model includes receiving a plurality of input images; andtraining a deep learning-based classification model based on thereceived input images to perform domain pixel-wise scene parsing of acluttered scene.

In one example, the domain pixel-wise scene parsing is for a particulardomain of the cluttered scene.

In one example, the cluttered scene includes a room layout.

In one example, wall-to-floor domain, wall-to-wall domain, orwall-to-ceiling domain of the room layout are predicted pixel-wise.

In one example, a relationship between domains of the room layout areintegrated into a convolutional neural network (CNN).

In one example, the CNN is trained in three incremental stages.

According to one example, a deep learning system includes a sensorarray, input buffer, network executor, and a training pipeline. Thesensor array is configured to capture a sequence of images. The inputbuffer is configured to receive the sequence of images. The networkexecutor has a plurality of cores and configured to execute a deeplearning network on the plurality of cores. The training pipelineframework is configured to operate according to any one or more of theabove examples.

According to one example, a machine-readable medium comprisinginstructions which when operated on by the machine cause the machine tooperate according to any one or more of the above examples.

According to one example, an apparatus includes a memory, a neuralnetwork, and a processor. The memory is configured to store inputinitial, intermediate, and final results. The processor is configured tocause, optimize, or configure the neural network to operate according toany one or more of the above examples.

According to one example, an apparatus includes memory means for storinginput initial, intermediate, and final results, logic means foroperating according to any one or more of the above examples.

According to one example, a method for optimizing the operation of afully connected network includes receiving features of a scene and aplurality of positions as conjunction points of features in the scene;determining gradients of an energy objective for the scene; and updatingthe positions of the conjunction points based on the gradients.

In on example, the energy objective is a comparison of the conjunctionpoints with a topology of an image of the scene.

In one example, the features and conjunction points are converted intoan edge label map of the image of the scene.

In one example, the topology of the image of the scene represents a setof possible scene feature arrangements.

In one example, the gradients iteratively approach a lowest energy fromaverage values of conjunction points at different positions.

In one example, the gradients are applied to endpoints of the featuresas a force applied to a spring-like edge.

In one example, the end points are used to approximate a gradient for anentire edge.

According to one example, an optimization method for a convolutionalneural network (CNN) includes receiving a sequence of training images inthe CNN to describe objects of a cluttered scene as a room layoutdataset; identifying features corresponding to potential edges ofobjects in the cluttered scene; and considering each potential edge as aspring which may translate, rotate and change its length.

In one example, the spring movements are approximated by interpolatingendpoints of each respective edge.

In one example, the interpolation is approximated with gradients.

In one example, approximating the interpolation includes determininggradients that mimic end points of the edge as particles moving in apotential field.

In one example, the forces of the spring are applied to a differentpotential field for each edge.

According to one example, a machine-readable medium comprisinginstructions which when operated on by the machine cause the machine tooperate according any one or more of the above examples.

According to one example, an apparatus comprises a memory configured tostore input initial, intermediate, and final results; a neural network;and a processor to cause, optimize, or configure the neural network tooperate according to any one or more of the above examples.

According to one example, an apparatus includes memory means for storinginput initial, intermediate, and final results; and logic means forperforming any one or more of the methods of the above examples.

According to one example, a data processing system includes a processingcore, an I/O hub controller, and a graphics processor. The processingcore is configured with a convolutional neural network (CNN). The I/Ohub controller is coupled to the processing core and configured toprovide network, data storage, and access to the processing core. Thegraphics processor is coupled to the I/O hub controller and configuredto receive features of a scene and a plurality of positions asconjunction points of features in the scene; determine gradients of anenergy objective for the scene; and update the positions of theconjunction points based on the gradients.

In one example, the energy objective is a comparison of the conjunctionpoints with a topology of an image of the scene.

In one example, the graphics processor is further configured to convertthe features and conjunction points into an edge label map of the imageof the scene.

In one example, the topology of the image of the scene represents a setof possible scene feature arrangements.

In one example, the gradients iteratively approach a lowest energy fromaverage values of conjunction points at different positions.

In one example, the gradients are applied to endpoints of the featuresas a force applied to a spring-like edge.

In one example, the end points are used to approximate a gradient for anentire edge.

The foregoing description and drawings are to be regarded in anillustrative rather than a restrictive sense. Various modifications andchanges may be made to the examples and embodiments described hereinwithout departing from the broader spirit and scope of the invention asset forth in the appended claims.

1. A pipeline framework to run deep learning training, the pipelineframework comprising: a first stage configured to receive a sequence oftraining images in a convolutional neural network (CNN) to describeobjects of a cluttered scene as a semantic segmentation mask; a secondstage configured to receive the semantic segmentation mask in a semanticsegmentation network and to produce semantic features; and a third stageconfigured to use weights from the first stage as feature extractors andweights from the second stage as classifiers in order to identify edgesof the cluttered scene using the semantic features.
 2. The frame ofclaim 1, wherein the third stage is further configured to fine-tune theCNN that receives the training images and identifies the edges of thecluttered scene.
 3. The framework of claim 1, wherein the second stageis further configured to use a softmax operation in the semanticsegmentation network.
 4. The framework of claim 1, wherein objects ofthe cluttered scene are part of a room layout dataset.
 5. The frameworkof claim 4, wherein the room layout dataset comprises pixels and whereinthe second stage is configured to treat each pixel of the room layoutdataset as a sample in a fully connected layer of the semanticsegmentation network.
 6. The framework of claim 4, wherein the secondstage is further configured to model relationships between the roomlayout dataset and objects in the CNN.
 7. The framework of claim 1,wherein the third stage is further configured to label edges of thecluttered scene.
 8. A data processing system comprising: a processingcore configured to implement a convolutional neural network (CNN); anI/O hub controller coupled to the processing core and configured toprovide network, data storage, and access to the processing core; agraphics processor coupled to the I/O hub controller and configured toimplement: a first stage configured to receive a sequence of trainingimages in a convolutional neural network (CNN) to describe objects of acluttered scene as a semantic segmentation mask; a second stageconfigured to receive the semantic segmentation mask in a semanticsegmentation network and to produce semantic features; and a third stageconfigured to use weights from the first stage as feature extractors andweights from the second stage as classifiers in order to identify edgesof the cluttered scene using the semantic features.
 9. The dataprocessing system of claim 8, wherein the third stage is furtherconfigured to fine-tune the CNN that receives the training images andidentifies the edges of the cluttered scene.
 10. The data processingsystem of claim 8, wherein the second stage is further configured to usea softmax operation in the semantic segmentation network.
 11. The dataprocessing system of claim 8, wherein objects of the cluttered scene arepart of a room layout dataset comprising pixels.
 12. The data processingsystem of claim 11, wherein the second stage is further configured totreat each pixel of the room layout dataset as a sample in a fullyconnected layer of the semantic segmentation network.
 13. The dataprocessing system of claim 11, wherein the second stage is furtherconfigured to model relationships between the room layout dataset andobjects in the CNN.
 14. The data processing system of claim 8, whereinthe third stage is further configured to label edges of the clutteredscene.
 15. A computation training method for a convolutional neuralnetwork (CNN) comprising: receiving a sequence of training images in theCNN of a first stage to describe objects of a cluttered scene as asemantic segmentation mask; receiving the semantic segmentation mask ina semantic segmentation network of a second stage to produce semanticfeatures; and using weights from the first stage as feature extractorsand weights from the second stage as classifiers in a third stage toidentify edges of the cluttered scene using the semantic features. 16.The method of claim 15, further comprising: fine-tuning the CNN in thethird stage.
 17. The method of claim 15, further comprising: using asoftmax operation in the semantic segmentation network of the secondstage.
 18. The method of claim 17, wherein objects of the clutteredscene are part of a room layout dataset comprising pixels.
 19. Themethod of claim 18, further comprising: treating each pixel in the roomlayout dataset as a sample in a fully connected layer of the semanticsegmentation network of the second stage.
 20. The method of claim 18,further comprising: modelling relationships in the second stage betweenthe room layout dataset and objects in the CNN.
 21. The method of claim15, further comprising: labeling edges of the cluttered scene pixel-wisein the third stage. 22-52. (canceled)